Advanced Spectroscopy Techniques with Chemometrics in Food Analysis

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Mourad Kharbach	--	6782	2023-07-31 08:41:21	\|
2	format correct	Catherine Yang	Meta information modification	6782	2023-07-31 10:24:37	\|

This entry is adapted from the peer-reviewed paper 10.3390/foods12142753

The growing world population is increasing the demand for food in multiple ways, which is leading to a higher demand for safety and quality control of commercialized products. Food can become contaminated by chemicals and physical substances through accidental or intentional means. Advanced spectroscopy techniques paired with chemometric tools are crucial in analyzing food by providing a fast, non-destructive, and efficient means of obtaining detailed information about food samples. This information can be used to improve the quality, safety, and authenticity of food products.

food analysis food authenticity food chemicals spectroscopy techniques Chemometrics Multivariate analysis food control

1. X-ray-Fluorescence-Based Methods

Energy dispersive X-ray spectroscopy (EDXRF) is a technique commonly used for determining mineral content in food samples. Additionally, its association with the unsupervised and supervised data analysis tools demonstrated its efficiency to deal with the challenges of food analysis. The scope of this section is to discuss the application and usefulness of X-ray-based spectroscopic techniques in combination with chemometric tools for qualitative and quantitative analysis of various food samples.

For example, ^[1] EDXRF has been applied with PLS Regression for analyzing the micronutrient zinc in biofortified banana samples. This method showed good results mainly on low limits of detection (LOD) and quantification (LOQ). Another research work, conducted by Gamela et al. ^[2] used the same combination of EDXRF and PLS to determine not only zinc but also the contents of copper and strontium in cocoa bean samples. The study proved satisfactory results through the evaluation of developed PLS models in terms of the same criteria. Additionally, this study has been extended and proved the ability of EDXRF to be fused with Laser Induced Breakdown Spectroscopy (LIBS) to determine the micronutrient potassium in cocoa beans using the supervised technique of multivariate calibration. This fusion showed besides the satisfactory results an advantage to minimize the matrix effect induced by samples of cocoa beans.

The combination of Energy-Dispersive X-ray Fluorescence (EDXRF) and chemometric tools have been used for qualitative purposes too. Galvan et al. ^[3] carried out an analysis by EDXRF under two measurement conditions, to classify the geographical area of two food species and also according to the production mode by PLS-DA. These food species were tomato and sweet pepper samples Based on the good results of classification EDXRF was considered an excellent technique for authentication of plant-based food products based on the mineral elements K, Ca, Mn, and Fe. Another study for the same qualitative purpose ^[4] carried out by the association of X-ray Fluorescence (XRF) to (PCA) permitted to identify elements like Cl, K, Ca, Fe, Br, Cl, Rb and Sr which establish a clear fingerprint pattern of the tomato. Similarly, other work applied several chemometrics tools for the discrimination of Italian Extra Virgin Olive Oil (EVOO) geo-markers through the analysis of mineral constituents using EDXRF and associated with PCA and SIMCA ^[5]. Besides EDXRF and XRF, Total-Reflection X-ray Fluorescence was also employed for food screening ^[6]. For example, different wine samples from two different geographical regions of Croatia were discriminated against based on the analysis of thirteen metal contents through the association of TXRF with PCA, cluster analysis, and Linear Discriminant Analysis (LDA). Thanks mainly to PCA, elements such as K, Mn, Ba, and Ni were determined as the most relevant to characterize between different origins of wines ^[7]. TXRF has already been associated with both PCA to obtain the clustering of the bean seeds according to their geographical origin, then it was coupled to PLS-DA for classification purposes ^[8]. Specific studies that utilize EDXRF in combination with chemometric tools for qualitative and quantitative analysis of various food samples are highlighted. The end points emphasize EDXRF’s efficiency in determining mineral content, addressing challenges in food analysis, and its application in food authentication and geographical classification.

XRF-based methods are commonly used in food quality control and analysis due to their non-invasive and time-efficient nature. They can simultaneously detect and quantify trace elements and contaminants in food. However, they have limitations such as limited sensitivity, making them unsuitable for some applications, and being a surface analysis technique, they may not provide information on deeper layers of the sample. The presence of other compounds in the food matrix may also interfere with the analysis, necessitating calibration and standardization to minimize such effects.

2. Hyperspectral and Multispectral Imaging

In contrast to traditional spectroscopy, hyperspectral imaging affords continuous and high-resolution narrow-band spectral data linked to both physical and chemical sample composition ^[9]. With The HSI, an object’s spectral and spatial information can be retrieved simultaneously by integrating spectroscopic and imaging techniques. This technique has immense potential and has been reported in the detection of various food adulteration, especially when it is associated with chemometric approaches for quantitative purposes. The scope of the proposed paragraph is to discuss the application and advantages of hyperspectral and multispectral imaging combined with chemometric tools in detecting food adulteration, assessing food composition, monitoring food quality, and classifying different food products.

Various studies have been conducted recently on wheat flour to estimate its different contents ^[10]. Unlike conventional methods, HSI is a reagent-free, non-invasive ^[11]. One of the special HSI characteristics is to exhibit metabolic transformations, making it useful to assess food composition. A significant amount of recent work has been focused on the application of HSI to various food and agricultural products and animal products. For instance, hyperspectral imaging was used within 400–800 nm to develop a method for analyzing impurities of mites in wheat flour through the supervised chemometric tool of ANN ^[12]. Benzoyl peroxide, which can also be found as a bleaching agent in wheat ^[13], was investigated using shortwave infrared (SWIR) HSI and PLS regression ^[14]. The estimation of talcum content has also been done using hyperspectral imaging and the SNV-PLS model, which proved to estimate adequately the talcum content ^[15]. In terms of food analyzed by HSI, research by Al-Sarayreh et al. investigated the efficiency of hyperspectral imaging systems to detect meat adulteration, depending on its storage conditions. This analysis proved efficiently the advantage of CNN compared to SVM for this analysis purpose ^[16]. In addition to wheat and meat, HSI has also been applied to other food samples such as cheese. Priyashantha et al. developed and evaluated a predictive model based on coupling the NIR-HS imaging technique and PLS for determining the maturity state of cheese. The model was then applied on a pixel-wise basis, producing prediction images, and allowing for the determination of how and where the maturity spread in the cheese ^[17]. On the same food product, PLS and Monte Carlo Cross Validation (MCCV) were applied to HSI to detect the main wavelengths of fat and microbial transglutaminase (mTG), which is responsible for the color and yield of the cheese. Additionally, this study mentioned the possibility of using HSI to inspect the cheese remotely through its transparent foil ^[18]. Potato is another food sample that has recently started to be useful for monitoring its quality with HSI. Lu et al. assessed the impact of storage times on the evolution of solanine content in potatoes by using HSI in the spectral region of 500–1000 nm and support vector regression (SVR) and then allowed estimating the edibility of the potatoes ^[19]. Besides solanine content, the color is another indicator, that is also considered as a parameter to judge the quality of potatoes. Xiao et al. combined HSI in the region of 477–947 nm and used and compare two supervised tools: (PLS) and (LS-SVM) for predicting this last parameter ^[20]. The sweet potato has been subjected to HSI analysis. Tian et al. investigated how the moisture and total anthocyanin contents of potato samples under various drying conditions, by using HSI in the region of 400–1000 nm and PLS. The obtained results of this method showed a low prediction error and a high R²p ^[21]. The use of HSI has been shown to be useful in predicting specific parameters that characterize food products. Recently, Li et al. analyzed the quality of plum fruit based on color and soluble solid content using HSI and PLSR and showed how short-wave infrared (SWIR) hyperspectral imaging can predict soluble solid content, ^[22]. Sun et al. monitored the quality of melon through its indicators as sweetness and hardness by associating NIR hyperspectral imaging system to PLSR ^[23].

Besides the quantitative advantages of HSI that have been cited, HSI has also been widely used for qualitative purposes. HSI in the spectral domain has been applied to detect chilling injury in agri-food products, which could not be achieved without subjecting HSI to multivariate data analysis. For example, Cen et al. employed HSI in reflectance (500–675 nm) and transmittance (675–1000 nm) modes with supervised classification tools for the detection of chilling injury in cucumbers ^[24]. Tsouvaltzis et al. evaluated the chilling injury in eggplant fruit by coupling visible and Near-Infrared (NIR) HSI to classification tools such as PLS-DA, SVM, and KNN to classify eggplant fruit according to storage temperature ^[25]. Recently, Babellahi et al. demonstrated the convenience of HSI with PLS-DA to discriminate between cold-stored green peppers that can be impacted by chilling injury and fresh ones ^[26]. Another example applied to fruits; peaches might have chill damage during cold storage which Pan et al. associated HSI to Artificial Neural Network (ANN) to differentiate normal peaches from chill-damaged ones ^[27]. Sun et al. assessed the classification of peaches based on the chilling injury by PLS-DA, SVM, and ANN with Spectral Angle Mapper (SAM) which achieved the best classification performances ^[28]. Related to the application of HSI on peaches, Li et al. investigated and compared Long-Wavelength-Near-Infrared (LW-NIR) and Short-Wavelength-Near-Infrared (SW-NIR) hyperspectral imaging by associating them with PCA and the approach of watershed segmentation for discriminating bruised from healthy peaches ^[29]. This study clearly proved the advantage of SW-NIR in detecting early bruises in peaches. Moreover, bruises have also been identified in blueberries samples by SWIR hyperspectral image and the developed models based on two approaches: considering Least Squares-Support Vector Machine (LS-SVM) with full spectra and optimum selected wavelengths by (CARS) (CARS-LS-SVM model) ^[30]. There are many factors that can lead to bruising food items. For example, Hyperspectral imaging technology applied in the region 400–1000 nm, was used with PLS-DA to classify bruised tomatoes that were caused by falling damage, detection times, falling heights or fruit sizes ^[31]. Besides fruits and vegetables, HSI with chemometric tools has shown its convenience in the qualitative analysis of wheat. Zhao et al. developed an approach based on a hybrid CNN model with hyperspectral imaging technology to classify different varieties of wheat seed ^[32]^[33]. In addition to wheat seeds, maize seeds were classified through the association of HSI with the chemometric approach of Radial Basis Function Neural Network (RBFNN) ^[34]. Soares et al. also presented a new strategy for fast and non-destructive classification of cotton, based mainly on coupling NIR-HSI images to PLS-DA. The results of this method showed good accuracy in the classification of test samples, with correct classification rates ^[35]. The endpoints of this section are to emphasize the potential of hyperspectral and multispectral imaging in providing continuous and high-resolution spectral data linked to physical and chemical composition, their non-invasive and reagent-free nature, and their ability to analyze various food samples.

Hyperspectral and multispectral imaging are valuable tools for food quality control and analysis, with advantages and limitations to consider. These imaging techniques offer non-destructive analysis and high spatial resolution for detailed surface analysis, simultaneous detection of multiple analytes, and real-time analysis for efficient quality control. However, hyperspectral, and multispectral imaging equipment can be expensive, have limited penetration for internal composition analysis, may lack sensitivity for detecting low analyte levels, and require complex image processing and specialized expertise for data analysis.

3. Infrared Spectroscopy

Infrared including (NIR) and (MIR) are ones of the conventional spectroscopy that have been usually used with many multivariate data analysis tools in food quantitative analysis. The scope of the proposed paragraph is to discuss the application and benefits of infrared spectroscopy with chemometrics, in quantitative and qualitative analysis of food components, such as carbohydrates, proteins, fats, and moisture content, as well as the determination of functional groups, carbon, and nitrogen.

MIR is responsible mainly for detecting functional groups as well as carbon and nitrogen, whereas NIR is used for determining carbohydrates, proteins, fats, and moisture content in various foods ^[36]. However, the method is not sensitive enough for samples containing just small amounts of target components. The fundamental vibrations occur when absorbed in the NIR ^[37]. Wang et al. combined NIR PLSR to estimate the content of potato flour in steamed bread ^[38]. Recently, the same association of NIR with PLS regression was applied to wheat flour samples to estimate the quantity of low-content talcum. In this study, several chemometrics were applied with PLS together to select the effective feature as genetic algorithm and elastic net, thus improving the capacity of the PLS model ^[39]. In addition to talcum, zearalenone might have an impact on the quality and safety of wheat grains. Recently, a study was carried out to determine zearalenone in wheat by NIR spectroscopy and (SVM) model. The results were significantly improved after the application of a variable selection approach called least absolute shrinkage and selection operator (LASSO) to extract useful spectral regions of NIR. In contrast to the contents that can have an impact on wheat, the determination of valuable contents has been featured in many recent research works ^[40]. Kamboj et al. predicted quality parameters mainly protein and carbohydrate of wheat content that has been stored at different temperatures using NIR Spectroscopy (NIRS) with PLS, MLR, and SVM ^[41]. Additionally, the fatty acid value is also considered an important indicator of the quality of wheat flour, particularly during storage. Therefore, Jiang et al. demonstrated the feasibility of using portable NIR spectroscopy in conjunction with appropriate chemometric methods to achieve quantitative determination of fatty acid values in wheat flour during storage. Jiang et al. used a method called variable combination population analysis (VCPA) in addition to PLS to improve NIR spectral characteristic wavelengths ^[42]. In addition to the chemometric tools that were cited and used for quantitative purposes, MCR-ALS is one of the chemometric tools that is combined with FT-NIR spectroscopy to estimate certain characteristics of food samples. For instance, an assessment of the combination of multivariate tools, including PLS regression and MCR-ALS, was used to predict antioxidant activity from clove and pomegranate extracts. The results showed that MCR-ALS with FT-NIR stood out among PLS with high R² and low RMSEP ^[43]. Another application of PLS and MCR associated with FT-NIR was used successfully to estimate peanut oil adulterants, ^[44]. Castro et al. also proved the efficiency of coupling FT-NIR with MCR-ALS for the quantitative purpose of four adulterants at low levels in a complex mixture of saffron, including onion, calendula, pomegranate, and turmeric ^[45]. In terms of determining adulterants in saffron, PLS-R was applied to FT-NIR data of saffron to estimate lotus stamens and corn stigmas. This study proved the efficiency of combining PLS with the variable selection approach of competitive adaptive reweighted sampling (CARS) showing good results ^[46]. Additionally, crocin I and II were analyzed using near-infrared spectroscopy and chemometrics. Crocin I and II are considered the most important indicators of the quality and commercial value of saffron ^[47]. Le et al. used FT-NIR and PLS to determine these two contents in saffron with low RMSECV ^[48].

Many studies have shown how MIR and NIR spectroscopy are efficient for the qualitative analysis of different food varieties comprised for example identification, classification, and authentication, based on, for example, country of origin. For instance, Liang et al. used NIR spectroscopy appropriately for the detection of zebra chip disease using Canonical Discriminant Analysis with a low classification error rate ^[49]. Discriminative analysis was applied to durum wheat to determine if they were contaminated by ochratoxin by combining FT-IR and FT-NIR with PLS-DA and PCA-LDA. In this study, FT-IR and FT-NIR were convenient spectroscopic techniques for discrimination purposes ^[50]. PLS-DA was compared to other classification tools such as HCA, SVM, and ANN to identify and classify Panax notoginseng with its adulterants. The classification purpose of this work was achieved by both PLS-DA and SVM with 100% classification accuracy ^[51]. PLS-DA showed its efficiency in detecting the freshness of rice based on storage time using FT-NIR with an accuracy of 96%, whereas the application of KNN achieved an accuracy of 100% ^[52]. In relation to the analysis of rice by NIR, L.-H. Xie et al. led a discrimination of two kinds of rice, waxy, which contains very low apparent amylose content, and non-waxy rice. The developed PLS-DA model allowed the recognition of these two types of rice with 100% accuracy ^[53]. Detecting fake eggs from authentic ones is another example that proves the efficiency of FT-IR and chemometrics in this field of food analysis. Joshi et al. showed how PLS-DA and SVM achieved a good classification of 100% ^[54]. The authenticity of the native was subjected to FT-NIR analysis by Chen et al. who proved the efficiency of using Data-Driven Class Modeling (DD-SIMCA) as an alternative tool for this classification ^[55]. The end points of the paragraph are to emphasize the effectiveness of infrared spectroscopy in estimating the content of target components in food samples, such as potato flour, talcum, zearalenone, protein, carbohydrate, fatty acid values, and antioxidant activity. The paragraph also mentioned the successful application of chemometric approaches in enhancing the accuracy and reliability of quantitative analysis using NIR and MIR spectroscopy.

Overall, infrared spectroscopy is a powerful and versatile tool for food quality control and analysis. However, limitations such as limited penetration, sample homogeneity requirements, calibration requirements, complexity of data analysis, and interference from other components should be taken into consideration when using this technique.

4. Raman Spectroscopy

Raman spectroscopy is a vibration spectroscopy technique that is based on monochromatic light diffusion. It involves the excitation of a sample by collisions with photons, which causes the sample to reach an unstable state of virtual energy. The scope of the proposed paragraph is to discuss the application of Raman spectroscopy in food analysis, both qualitatively and quantitatively.

Raman spectroscopy was carried out for the determination of fat content in various food samples such as milk and meat. Heterogeneous foods have recently been detected chemically with Raman microscopy ^[56]. This combination is used qualitatively and quantitatively to evaluate food value. Many organic components are detected and identified based on the absorption curves. Microscopic food species can be analyzed too. Raman microscopy has been carried out to determine the main composition of wheat and to detect protein content changes during milling ^[57]^[58]. Raman spectroscopy detects changes in protein secondary structure, conformational changes in lipid-binding proteins.

Based on the Raman spectrum, it is possible to estimate the relative concentration of food contents. For instance, a recent study aimed to determine starch using Raman spectroscopy and a linear regression model for a specific band, and PLS regression for a specific spectral region ^[59], which confirmed the efficiency of association of FT-Raman to PLS for the estimation of gluten content in flour ^[60]. Carotenoids have been determined in tomatoes by Raman spectroscopy and PLS regression and proved low prediction error ^[61]. The main characteristic of Raman spectroscopy is that it can directly measure aqueous solutions because of the low effect of water, and even the sample preparation of liquids for Raman analysis is simple, which can be considered an advantage to estimating the contents in food liquids such as milk ^[62]. Whey is one of the contents that has been quantified accurately in the milk ^[63]. In addition to whey, macronutrients such as fat, lactose, and protein have been successfully quantified in commercial yoghurt samples using FT-Raman spectroscopy and PLS models ^[64]. In a recent study, milk adulterants such as sodium bicarbonate, maltodextrin, and whey were also analyzed using Raman spectroscopy and the PLS chemometric tool, with a low detection limit ^[65]. A handheld Raman spectrometer has also been applied to quantify lard in adulterated butter, another milk derivative, through PLS ^[66]. Richardson et al. demonstrated how Raman spectroscopy is able with PLS to detect coconut water adulteration ^[67]. In a recent study, various variable selection approaches were tested on surface-enhanced Raman scattering spectra of rice, used with PLS for quantifying the target residue analyte of chlorpyrifos ^[68].

As it has shown its relevance for quantitation, Raman spectroscopy has proven its efficiency with chemometric tools in many recent studies. For example, Robert et al. built a classification model using SVM and PLS-DA to discriminate lamb meat from beef meat despite the similar chemical composition of these two species ^[69]. Hai Chao et al. classified duck meat according to the residues of testosterone propionate and testosterone nandrolone using Raman Spectroscopy and Support Vector Classification (SVC) which shows a classification rate of 100% for the test set ^[70]. Other residues that have an impact on duck meat and have been subjected to analysis by means of Raman spectroscopy and chemometrics are sulfonamides, comprised of sulfadimidine and sulphapyridine ^[71]. A recent research work used a support vector classification on Raman Spectroscopic data to classify duck meat into four groups, which are as follows: samples free of residues, samples containing one of the two residues, and samples containing both residues ^[72]. Another mode of Raman spectroscopy called Spatially Offset Raman Spectroscopy, which allows to measure the chemical compounds under the surface of meat tissues ^[73]. Besides that, a study has also proven that the use of Raman spectroscopy in combination with the SVM method can discriminate rice samples according to their regions with high-rate accuracy ^[74]. Raman was applied to differentiate four categories of milk species of cow, buffalo, goat, and human. Thus, Principal Component Analysis (PCA) besides Random Forest (RF) was applied on Raman data to highlight and characterize the Raman spectra of different milk samples with high accuracy of 93.7% ^[75]. In addition to benchtop Raman, Handheld Raman spectroscopic devices have shown their efficiency using SIMCA to classify milk samples from adulterated ones ^[76]. In addition to milk and its derivative samples, PLS-DA was employed with Raman to accurately classify a milk derivative of cheese whether it was adulterated by starch or not ^[77]. Related to handheld Raman, Aykas, et al. succeeded in seeking to characterize commercial honey by combining handheld Raman equipment and SIMCA ^[78]. A recent research work monitors according to a new method the adulteration in cassava starch, by means of Raman spectroscopy and supervised tool One-Class Modelling (OC-SVM) which proved its higher accuracy compared to the SIMCA, allowing for the discrimination of samples ^[79]. Discriminant analysis by PLS-DA of coffee genotypes by Raman spectroscopy based on two main contents, kahweol and fatty acids, has shown how Raman with chemometrics was more effective compared to sensorial analysis ^[80]. Sha et al. combined Raman with PCA, HCA, and SVM for feature extraction to improve the efficiency of identification of rice varieties ^[33]. For oil samples, Jiménez-Carvelo et al. used chemometrics for the classification and characterization of pure olive oil from adulterated using Raman spectroscopy in addition to NIR by employing classification models. While PCA was used to reduce the features, other supervised techniques were applied to for the discrimination goal ^[81]. Raman analysis was also employed to discriminate waste cooking oil from edible vegetable oil. Thanks to PCA, signals at 869, 969, 1302, and 1080 cm⁻¹ were found to be the most important features to differentiate between these two types of oils. In addition, PCA demonstrated its ability to separate adulterated from pure oils when the adulteration proportions reached 10% and 20% ^[82]. The endpoints of this part include the successful application of Raman spectroscopy in estimating the content of specific food components and the detection and classification of various residues and adulterants in food samples.

Finally, Raman spectroscopy is a valuable method for analyzing and controlling the quality of food, offering several advantages such as non-destructiveness, molecular specificity, sensitivity, minimal sample preparation, and high spatial resolution. However, when using this technique, some limitations must be considered, including its limited penetration depth, susceptibility to fluorescence interference, equipment cost, complexity of data analysis, and sensitivity to water.

5. NMR Analysis

Nuclear magnetic resonance (NMR) is a spectroscopic technique used to determine the molecular structure and physical properties of substances, and efficiently used to ensure the quality of different varieties of food samples ^[83]. The scope of the proposed paragraph is to discuss the application of NMR spectroscopy in food analysis, both qualitatively and quantitatively. It focuses on the use of NMR spectroscopy and chemometric tools for food identification, discrimination, and characterization purposes.

For example, the combination of low and high-field NMR and chemometrics, including PLS-R and SVR, has proved its ability to accurately estimate essential quality parameters of edible oils, especially to detect potential adulteration. The results summarized in statistical parameters indicate that all developed models, whether of PLS-R or SVR on the three different fields of NMR, were similar. In addition to oil applications, Haddad et al. have carried out a quantitative analysis of fatty acids based on ¹H-NMR variables as predictors and relative mass percentages of fatty acids as targets, including caproic, caprylic, capric, oleic, palmitic, and margaric ^[84]. Fatty acids have been accurately estimated in hen egg samples by ¹H-NMR and PLS regression ^[85]. Proton nuclear magnetic resonance (¹H-NMR) associated with chemometrics were combined to investigate the camellia oil adulterants with other vegetable oils ^[86]. In addition to ¹H-NMR, ¹H TD-NMR was combined efficiently with PLS regression to detect the percentage of adulterants such as water and whey in milk products varied from 5% to 50% through milk package and without sample preparation ^[87]. Besides PLS regression, Sun et al. successfully set up a model to detect moisture content through the association of low-field NMR and ANN with a low RMSE ^[88].

As previously shown, NMR spectroscopy supported by multivariate data analysis tools has been applied for various qualitative purposes in different foods. For example, Milani et al. successfully explored the versatility of 1H NMR with pattern recognition of PCA and SIMCA for identification and discrimination of pure Brazilian coffee from adulterated ones by corn, barley, or even coffee husks. The built SIMCA model ensured its high classification accuracy ^[89]. In relation to these quality analysis of coffee, 1H NMR data of roasted coffee samples were analyzed qualitatively by OPLS-DA to characterize organic roasted coffee from conventional coffee. The orthogonal signal correction (OSC) allowed for the extraction of the main features of each coffee category and thus improved the PLS-DA model discrimination. While fatty acids, β-(1-3)-d-galactopyranose, quinic acid, and its cyclic ester were the major metabolites characterizing organic roasted coffee, conventional coffee was characterized mainly by trigonelline and chlorogenic acid isomers ^[90]. The OSC filter was used with PCA (OSC-PCA) and applied to HR MAS 1H NMR data of cocoa beans to discriminate them based on their origin, whether they were American or African, based on the fatty acids, acetate, and saccharides components ^[91]. In another research work, both 1H NMR and 13C NMR were employed to analyze refined edible oils from different sources. By applying PCA on the 1H NMR or on 13C NMR, it was possible to identify and characterize these plants based on their fatty acids ^[92]. Amino acids were analyzed by NMR and explored by chemometric tools in fruits, since they are considered essential metabolites in cell function and enable distinction between plants of the same fruit. For example, Botoran et al. identified ten kinds of amino acids that allowed for the observation of differences and distinctions of different varieties of juice using PCA and LDA, which accurately classified juices from different plant sources ^[93]. In the honey adulteration problem, Rachineni et.al analyzed successfully honey by associating 1H NMR with supervised machine learning (neural network) for the characterization purpose of authentic honey from the adulterated whether by sugar, brown rice syrup or jaggery syrup ^[94]. The endpoints of the paragraph include the successful application of NMR spectroscopy combined with chemometrics, and machine learning, in accurately estimating and detecting various quality parameters and adulterants in food samples.

Overall, NMR spectroscopy is a valuable tool for food quality control and analysis, offering numerous advantages such as non-destructiveness, molecular specificity, sensitivity, and versatility. Additionally, it allows for quantitative analysis, making it particularly useful for determining the concentration of specific compounds in food products. However, the technique also has limitations that should be considered, including equipment cost, limited penetration, sample preparation requirements, and sensitivity to sample properties.

6. UV-Visible

UV-visible spectroscopy is known as one of the most sensitive techniques for determining less concentrated contents in food samples. Its association with multivariate tools offers an added advantage for such quantitative analysis. This technique uses electromagnetic radiation between 200 and 800 nm and detects two different aspects: color and fat oxidation ^[95]. The scope of the proposed paragraph is to discuss the application of UV-visible spectroscopy in food analysis, particularly for quantitative and qualitative purposes.

In addition to other analytical techniques, a recent research work for the same food product employed UV-Vis with PLS regression to accurately determine squalene in Extra virgin olive oils (EVOO) ^[96]. Wu et al. integrated empirical mode decomposition with SVR (EMD-SVR) to evaluate the quality of edible blend oils samples, concluding that EMD-SVR was more accurate for the quantitative analysis of ternary edible blend oil ^[97]. Zhang et al. developed models by coupling UV-Vis to Partial least squares regression (PLS) and principal component regression (PCR) for the quantitation of acid value in various oils. The PLS models performed well compared to PCR models ^[98]. In addition to different oil analyses, UV-Vis spectroscopy was proved to be more efficient as a method associated with PLS instead of univariate tools for the quantitation of grape-must caramel in Balsamic vinegars of different varieties of wine vinegars ^[99].

The UV-Vis spectroscopy has been combined with multivariate techniques for various qualitative purposes in food analysis. For instance, UV-Vis combined with MCR-ALS is a suitable tool to pursue the autoxidation of edible oils and to monitor the quality of extra virgin olive oil (EVOO) in different packaging systems ^[100]. In addition to olive oil samples, multivariate discrimination tools using UV-Vis spectroscopy, such as PLS-DA and SVM, have been used to distinguish between two specific mint species, such as spearmint and peppermint, while SIMCA has been used to detect outlier samples other than the two species ^[101]. Similarly, coffee has been analyzed by UV-Vis spectroscopy and SIMCA to accurately classify and discriminate between Peaberry and normal coffee ^[102]. In addition to SIMCA and PLS-DA, artificial neural networks (ANN) have been applied to UV-Vis spectroscopy to discriminate between vinegars produced from different raw materials, showing the discrimination efficiency compared to PLS-DA ^[103]. Another study used UV-Vis spectroscopy and ANN to discriminate between vinegars adulterated with spirit vinegar or acetic acid ^[104]. The end points of the paragraph include the successful application of UV-visible spectroscopy combined with multivariate techniques for food analysis adulteration, improved discrimination, and classification purposes.

Generally, UV-Visible spectroscopy is a valuable tool for food quality control and analysis, with several advantages such as simplicity, non-destructiveness, versatility, and cost-effectiveness. However, it has limitations in sensitivity, interference, and surface analysis that should be considered while using this technique.

7. Fluorescence Spectroscopy

Fluorescence spectroscopy is a technique that focuses mainly on the molecular level. It refers to the process in which a specific wavelength of light is irradiated in a solution, and the fluorescent substance in the solution absorbs the released energy. The scope of the proposed paragraph is to discuss the application of fluorescence spectroscopy in food analysis, both for quantitative and qualitative purposes. It highlights the molecular-level focus of fluorescence spectroscopy and its ability to detect and analyze various elements in food samples.

In recent years, fluorescence spectroscopy has been applied for the analysis of various elements of food. For example, a recent research study exhibited the application of front-face fluorescence mode spectroscopy and supervised PLS to estimate cow milk adulteration with other milk kind ^[105]. Another study used excitation-emission matrix (EEM) fluorescence spectroscopy and second-order calibration ways, like (PARAFAC) and (U-PLS), to detect and estimate the content of melamine in milk ^[106]. Additionally, fluorescence spectroscopy has been used to detect and quantify adulteration in olive oils ^[107]. Three-dimensional fluorescence spectra were subjected to analyze the same analysis purpose using the supervised approach of GA-SVR ^[108].

Many studies have shown the potential of fluorescence spectroscopy combined with multivariate data analysis tools for the qualitative analysis of various food samples. For example, Yuan et al. ^[109] conducted a comparative study using excitation-emission matrix fluorescence, FTIR, and vis-NIR on different types of vegetable oils for discrimination purposes using advanced chemometric tools (PCA, multiway-PCA, PLS-DA, and unfold-PLS-DA). The study found that FTIR and Vis-NIR, were more suitable compared to EEM for the identification of vegetable oil species. This is because most chemical components in vegetable oil produce FTIR and NIR absorption, while only a small number of fluorophores produce fluorescence ^[109]. Another study proved the same classification results of these techniques for detecting olive oil adulteration. This highlights the importance of combining analytical techniques with the appropriate chemometric tool ^[110]. Fluorescence spectroscopy has been combined with chemometrics to distinguish pure Aroeira honey from adulterated. The advanced chemometric methods (PARAFAC, PLS-DA, unfolded PLS-DA (UPLS-DA), and N-way PLS-DA (NPLS-DA)) were used to decompose the spectral data and build classification models. This qualitative analysis has proven the convenience of fluorescence spectroscopy with UPLS-DA for this kind of honey analysis ^[111]. It can be noted from previous research that multi-way chemometric techniques are often applied conveniently to EEMs data, whether on edible oils, honey, or beverages, as demonstrated by Fang et al. for the classification of Chinese lager beers made by different manufacturers ^[112]. The end points of the paragraph include the successful application of fluorescence spectroscopy combined with multivariate data analysis tools for quantitative and qualitative analysis of various food samples.

Fluorescence spectroscopy is a powerful tool for food quality control and analysis, offering advantages such as high sensitivity, specificity, non-destructiveness, and rapid analysis. However, there are certain limitations that should be considered when using this technique, including the complexity of sample preparation, potential interference from other compounds, limited penetration depth, and high instrumentation costs.

8. Fusion of Spectroscopic Techniques

In recent years, the strategy of data fusion combined with multivariate statistical analysis, that has been widely used to ensure the safety of food and to extract more information for both qualitative and quantitative purposes. The scope of the proposed paragraph is to discuss the application of data fusion combined with multivariate statistical analysis in food analysis for both qualitative and quantitative purposes. It highlights the use of various spectroscopic techniques such as UV-VIS, NIR, Raman, FT-IR, FT-Raman, and MID, and their fusion with multivariate statistical models for food analysis.

A recent research work developed PLS and ANN models for the quantification of adulteration in honey, using data fusion of non-pre-processed UV-VIS and NIR spectra ^[113]. Vis-NIR and Raman have also been merged and applied to predict the storage time of infant formula between 0–12 months ^[114]. UV-Vis-NIR was combined with PLS to accurately quantify cholesterol in egg yolk, whether in the shell or in pasteurized form ^[115]. Additionally, the combination of PLS regression with the data fusion of FT-IR with Raman spectroscopy allowed the determination of peroxide values and acid values in oils ^[116]. A study was elaborated to test merging mid-infrared (MIR) with Raman spectroscopy for the fructose syrup determination in honey samples. The PLS model was used to estimate the adulterant, and the results were improved after the data fusion compared to the results obtained by each of the two spectroscopic techniques ^[117]. The same conclusion was achieved by a recent research work that evaluated the data fusion of NIR and MIR, combined with the sequential orthogonalized partial least square regression (SOPLS), to estimate different quality traits of tubers and root flours. These traits included different chemical compounds including for example amylose and protein ^[118].

The efficiency of data fusion methodology for qualitative purposes has been proved by Yao et al., who established a method based mainly on Fourier transform infrared (FT-IR) and ultraviolet (UV) spectroscopies associated with data fusion to distinguish different regions of mushroom samples ^[119]. A synergistic strategy of FT-Raman and NIR for the classification of two classes of hazelnut: unadulterated and adulterated with almonds using SIMCA was also demonstrated. The obtained results proved that merging the two techniques can be more effective than using each technique alone, based on sensitivity and specificity ^[120]. NIR and MID were also used with the SVM model to discriminate natural honey from syrup-adulterated one. In this case study, Huang et al. showed two levels of data fusion. A low level, in which redundant and irrelevant variables were introduced, and an intermediate level, where PCA was applied to extract the feature variables. The results acquired from this study had a significant increase in SVM model parameters of accuracy, precision, and sensitivity using the intermediate-level data fusion ^[121]. The endpoints of this section include the successful application of data fusion methodologies with chemometrics for quantification of adulteration, for estimating different chemicals, as well as for qualitative purposes and classifying unadulterated and adulterated food samples. However, there are limitations to consider when using data fusion methodologies in food analysis. The success of data fusion relies on the compatibility and complementarity of the combined techniques and the availability of appropriate statistical models. Proper calibration and validation procedures are necessary to ensure the reliability and robustness of the fused data. Furthermore, data fusion may introduce additional complexity and computational requirements, requiring careful data preprocessing and analysis. It is also important to consider the specific requirements and limitations of each spectroscopic technique and statistical model being used for data fusion.

The fusion of spectroscopic techniques provides an advanced tool for food quality control and analysis, offering advantages such as enhanced accuracy, complementary information, improved sensitivity, and non-destructiveness. However, it should be noted that this technique has some limitations, such as complexity, high equipment cost, sample preparation requirements, and limited penetration depth. These factors should be considered before implementing this technique for food analysis.

9. Portable Spectroscopic Techniques

The food industry is constantly seeking faster, more accurate ways to assess the safety and quality of their products while also detecting possible adulteration. Portable spectroscopic equipment, such as Raman, NIR and HSI among other techniques, have become increasingly popular due to their portability, accuracy, and ability to control food products ^[122]. The scope of the proposed paragraph is to discuss the application of portable spectroscopy techniques in the food industry for assessing food safety and quality, detecting adulteration, and enabling in-process monitoring. It highlights the advantages of portable spectroscopy equipment, such as Raman, NIR, and HSI, including their portability, accuracy, low sample preparation requirements, and cost-effectiveness.

Portable spectroscopies equipment in general requires less sample preparation and less hazardous consumption than traditional laboratory-based processes, granting fast results on food quality and safety. Furthermore, these technologies provide the food industry the low cost-effective analysis and product safety ^[123]^[124]. Portable spectroscopy techniques are highly effective in detecting food fraud and contaminants, such as pesticides, heavy metals, and pathogens. Previous developments in portable fluorescence and Raman spectroscopy have enabled water detection in milk and honey products with less expensive syrups, respectively ^[125]^[126]. Moreover, they are involved to analyze food quality attributes, for instance, mid-infrared spectroscopy used for fatty acid profile and fat content in lamb meat ^[125], and the amount of fat in meat and its degree of tenderness ^[127]. The NIR-HSI as a portable technique coupled with chemometric tools was showed excellent application in different food products ^[128]^[129].

Portable spectroscopy techniques were demonstrated to be a valuable solution for food manufacturers and processors, as they can be used for in-process monitoring of food quality and safety. The technology can also be used for post-harvest processing, such as the detection of mold and fungal infections in food products ^[130]. In addition, they have demonstrated to be versatile in identifying various food tampering and contaminants, including pesticides, heavy metals, and biological contaminants. They are also beneficial with chemometrics for quality control testing and authentication for rapid food chain analysis aimed at a perfect digital traceability system ^[124]. For a deep understanding, a recent review article provides an overview of how miniaturized NIR spectroscopy can be applied to address a range of issues in food-related settings ^[131]. They provide a comprehensive summary of the latest research trends, highlighting key factors driving the development of the micro-NIR analytical framework for modern food analysis, quality control, and safety risk monitoring. Emphasis is placed on the significance of combining complementary tools with the NIR analytical method, which enhances its precision, dependability, and versatility for food applicability. The endpoints of this section include the successful use of portable spectroscopy techniques and the use of chemometric tools for detecting food fraud and contaminants, analyzing food quality attributes, monitoring in-process food quality, safety, and rapid food chain analysis.

Finally, to highlight portable spectroscopy tools have several advantages in food applications, including non-destructive sample analysis, rapid analysis where timely decisions are needed, in situ analysis which is particularly useful in food-based applications. However, there are also some limitations to the use of portable spectroscopy techniques in food applications, such as limited accuracy particularly for complex samples, limited range of wavelengths which may not be suitable for all applications, regular calibration requirements which can be time-consuming, sensitivity to environmental conditions such as temperature and humidity, and often more cost-effective than traditional laboratory-based techniques ^[124]. These limitations should be taken into account when implementing portable spectroscopy in food analysis and quality control processes.

References

Sperança, M.A.; Mayorquín-Guevara, J.E.; da Cruz, M.C.P.; de Almeida Teixeira, G.H.; Pereira, F.M.V. Biofortification Quality in Bananas Monitored by Energy-Dispersive X-ray Fluorescence and Chemometrics. Food Chem. 2021, 362, 130172.
Gamela, R.R.; Pereira-Filho, E.R.; Pereira, F.M.V. Minimal-Invasive Analytical Method and Data Fusion: An Alternative for Determination of Cu, K, Sr, and Zn in Cocoa Beans. Food Anal. Methods 2021, 14, 545–551.
Galvan, D.; de Andrade, J.C.; Effting, L.; Lelis, C.A.; Melquiades, F.L.; Bona, E.; Conte-Junior, C.A. Energy-Dispersive X-ray Fluorescence Combined with Chemometric Tools Applied to Tomato and Sweet Pepper Classification. Food Control 2023, 143, 109326.
Panebianco, S.; Mazzoleni, P.; Barone, G.; Musumarra, A.; Pellegriti, M.G.; Pulvirenti, A.; Scordino, A.; Cirvilleri, G. Feasibility Study of Tomato Fruit Characterization by Fast XRF Analysis for Quality Assessment and Food Traceability. Food Chem. 2022, 383, 132364.
Scatigno, C.; Festa, G. A First Elemental Pattern and Geo-Discrimination of Italian EVOO by Energy Dispersive X-ray Fluorescence and Chemometrics. Microchem. J. 2021, 171, 106863.
Borgese, L.; Bilo, F.; Tsuji, K.; Fernández-Ruiz, R.; Margui, E.; Streli, C.; Pepponi, G.; Stosnach, H.; Yamada, T.; Vandenabeele, P.; et al. First Total Reflection X-ray Fluorescence Round-Robin Test of Water Samples: Preliminary Results. Spectrochim. Acta Part B Spectrosc. 2014, 101, 6–14.
Vitali Čepo, D.; Karoglan, M.; Borgese, L.; Depero, L.E.; Marguí, E.; Jablan, J. Application of Benchtop Total-Reflection X-ray Fluorescence Spectrometry and Chemometrics in Classification of Origin and Type of Croatian Wines. Food Chem. X 2022, 13, 100209.
Allegretta, I.; Squeo, G.; Gattullo, C.E.; Porfido, C.; Cicchetti, A.; Caponio, F.; Cesco, S.; Nicoletto, C.; Terzano, R. TXRF Spectral Information Enhanced by Multivariate Analysis: A New Strategy for Food Fingerprint. Food Chem. 2023, 401, 134124.
Xue, X.; Chen, Z.; Wu, H.; Gao, H. Identification of Guiboutia Species by NIR-HSI Spectroscopy. Sci. Rep. 2022, 12, 11507.
Tao, F.; Ngadi, M. Recent Advances in Rapid and Nondestructive Determination of Fat Content and Fatty Acids Composition of Muscle Foods. Crit. Rev. Food Sci. Nutr. 2018, 58, 1565–1593.
Wu, D.; Sun, D.-W. Advanced Applications of Hyperspectral Imaging Technology for Food Quality and Safety Analysis and Assessment: A Review—Part I: Fundamentals. Innov. Food Sci. Emerg. Technol. 2013, 19, 1–14.
He, P.; Wu, Y.; Wang, J.; Ren, Y.; Ahmad, W.; Liu, R.; Ouyang, Q.; Jiang, H.; Chen, Q. Detection of Mites Tyrophagus putrescentiae and Cheyletus eruditus in Flour Using Hyperspectral Imaging System Coupled with Chemometrics. J. Food Process. Eng. 2020, 43, e13386.
Qin, J.; Kim, M.S.; Chao, K.; Gonzalez, M.; Cho, B.-K. Quantitative Detection of Benzoyl Peroxide in Wheat Flour Using Line-Scan Macroscale Raman Chemical Imaging. Appl. Spectrosc. 2017, 71, 2469–2476.
Kim, G.; Lee, H.; Baek, I.; Cho, B.-K.; Kim, M.S. Quantitative Detection of Benzoyl Peroxide in Wheat Flour Using Line-Scan Short-Wave Infrared Hyperspectral Imaging. Sens. Actuators B Chem. 2022, 352, 130997.
He, H.-J.; Chen, Y.; Li, G.; Wang, Y.; Ou, X.; Guo, J. Hyperspectral Imaging Combined with Chemometrics for Rapid Detection of Talcum Powder Adulterated in Wheat Flour. Food Control 2023, 144, 109378.
Al-Sarayreh, M.; Reis, M.M.; Yan, W.Q.; Klette, R. Potential of Deep Learning and Snapshot Hyperspectral Imaging for Classification of Species in Meat. Food Control 2020, 117, 107332.
Priyashantha, H.; Höjer, A.; Saedén, K.H.; Lundh, Å.; Johansson, M.; Bernes, G.; Geladi, P.; Hetta, M. Use of Near-Infrared Hyperspectral (NIR-HS) Imaging to Visualize and Model the Maturity of Long-Ripening Hard Cheeses. J. Food Eng. 2020, 264, 109687.
Darnay, L.; Králik, F.; Oros, G.; Koncz, Á.; Firtha, F. Monitoring the Effect of Transglutaminase in Semi-Hard Cheese during Ripening by Hyperspectral Imaging. J. Food Eng. 2017, 196, 123–129.
Lu, B.; Sun, J.; Yang, N.; Hang, Y. Fluorescence Hyperspectral Image Technique Coupled with HSI Method to Predict Solanine Content of Potatoes. J. Food Process. Preserv. 2019, 43, e14198.
Xiao, Q.; Bai, X.; He, Y. Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis. Foods 2020, 9, 94.
Tian, X.-Y.; Aheto, J.H.; Bai, J.-W.; Dai, C.; Ren, Y.; Chang, X. Quantitative Analysis and Visualization of Moisture and Anthocyanins Content in Purple Sweet Potato by Vis–NIR Hyperspectral Imaging. J. Food Process. Preserv. 2021, 45, e15128.
Li, B.; Cobo-Medina, M.; Lecourt, J.; Harrison, N.; Harrison, R.J.; Cross, J. V Application of Hyperspectral Imaging for Nondestructive Measurement of Plum Quality Attributes. Postharvest Biol. Technol. 2018, 141, 8–15.
Sun, M.; Zhang, D.; Liu, L.; Wang, Z. How to Predict the Sugariness and Hardness of Melons: A near-Infrared Hyperspectral Imaging Method. Food Chem. 2017, 218, 413–421.
Cen, H.; Lu, R.; Zhu, Q.; Mendoza, F. Nondestructive Detection of Chilling Injury in Cucumber Fruit Using Hyperspectral Imaging with Feature Selection and Supervised Classification. Postharvest Biol. Technol. 2016, 111, 352–361.
Tsouvaltzis, P.; Babellahi, F.; Amodio, M.L.; Colelli, G. Early Detection of Eggplant Fruit Stored at Chilling Temperature Using Different Non-Destructive Optical Techniques and Supervised Classification Algorithms. Postharvest Biol. Technol. 2020, 159, 111001.
Babellahi, F.; Paliwal, J.; Erkinbaev, C.; Amodio, M.L.; Chaudhry, M.M.A.; Colelli, G. Early Detection of Chilling Injury in Green Bell Peppers by Hyperspectral Imaging and Chemometrics. Postharvest Biol. Technol. 2020, 162, 111100.
Pan, L.; Zhang, Q.; Zhang, W.; Sun, Y.; Hu, P.; Tu, K. Detection of Cold Injury in Peaches by Hyperspectral Reflectance Imaging and Artificial Neural Network. Food Chem. 2016, 192, 134–141.
Sun, Y.; Gu, X.; Sun, K.; Hu, H.; Xu, M.; Wang, Z.; Tu, K.; Pan, L. Hyperspectral Reflectance Imaging Combined with Chemometrics and Successive Projections Algorithm for Chilling Injury Classification in Peaches. LWT 2017, 75, 557–564.
Li, J.; Chen, L.; Huang, W. Detection of Early Bruises on Peaches (Amygdalus persica L.) Using Hyperspectral Imaging Coupled with Improved Watershed Segmentation Algorithm. Postharvest Biol. Technol. 2018, 135, 104–113.
Fan, S.; Li, C.; Huang, W.; Chen, L. Detection of Blueberry Internal Bruising over Time Using NIR Hyperspectral Reflectance Imaging with Optimum Wavelengths. Postharvest Biol. Technol. 2017, 134, 55–66.
Sun, Y.; Pessane, I.; Pan, L.; Wang, X. Hyperspectral Characteristics of Bruised Tomatoes as Affected by Drop Height and Fruit Size. LWT 2021, 141, 110863.
Zhao, X.; Que, H.; Sun, X.; Zhu, Q.; Huang, M. Hybrid Convolutional Network Based on Hyperspectral Imaging for Wheat Seed Varieties Classification. Infrared Phys. Technol. 2022, 125, 104270.
Sha, M.; Zhang, D.; Zhang, Z.; Wei, J.; Chen, Y.; Wang, M.; Liu, J. Improving Raman Spectroscopic Identification of Rice Varieties by Feature Extraction. J. Raman Spectrosc. 2020, 51, 702–710.
Zhao, Y.; Zhu, S.; Zhang, C.; Feng, X.; Feng, L.; He, Y. Application of Hyperspectral Imaging and Chemometrics for Variety Classification of Maize Seeds. RSC Adv. 2018, 8, 1337–1345.
Carreiro Soares, S.F.; Medeiros, E.P.; Pasquini, C.; de Lelis Morello, C.; Harrop Galvão, R.K.; Ugulino Araújo, M.C. Classification of Individual Cotton Seeds with Respect to Variety Using Near-Infrared Hyperspectral Imaging. Anal. Methods 2016, 8, 8498–8505.
Alamprese, C.; Casale, M.; Sinelli, N.; Lanteri, S.; Casiraghi, E. Detection of Minced Beef Adulteration with Turkey Meat by UV–Vis, NIR and MIR Spectroscopy. LWT-Food Sci. Technol. 2013, 53, 225–232.
Tammer, M.G. Sokrates: Infrared and Raman Characteristic Group Frequencies: Tables and Charts; Wiley: Chichester, UK, 2004.
Wang, H.; Lv, D.; Dong, N.; Wang, S.; Liu, J. Application of Near-Infrared Spectroscopy for Screening the Potato Flour Content in Chinese Steamed Bread. Food Sci. Biotechnol. 2019, 28, 955–963.
Du, C.; Sun, L.; Bai, H.; Zhao, Z.; Li, X.; Gai, Z. Quantitative Detection of Talcum Powder in Wheat Flour Based on Near-Infrared Spectroscopy and Hybrid Feature Selection. Infrared Phys. Technol. 2022, 123, 104185.
Ning, H.; Wang, J.; Jiang, H.; Chen, Q. Quantitative Detection of Zearalenone in Wheat Grains Based on Near-Infrared Spectroscopy. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2022, 280, 121545.
Kamboj, U.; Guha, P.; Mishra, S. Comparison of PLSR, MLR, SVM Regression Methods for Determination of Crude Protein and Carbohydrate Content in Stored Wheat Using near Infrared Spectroscopy. Mater. Today Proc. 2022, 48, 576–582.
Jiang, H.; Liu, T.; Chen, Q. Quantitative Detection of Fatty Acid Value during Storage of Wheat Flour Based on a Portable Near-Infrared (NIR) Spectroscopy System. Infrared Phys. Technol. 2020, 109, 103423.
Câmara, A.B.F.; de Oliveira, K.G.; Santos, M.C.D.; de Lima, R.R.S.; de Lima, K.M.G.; de Carvalho, L.S. Multivariate Assessment for Predicting Antioxidant Activity from Clove and Pomegranate Extracts by MCR-ALS and PLS Models Combined to IR Spectroscopy. Food Chem. 2022, 384, 132321.
Castro, R.C.; Ribeiro, D.S.M.; Santos, J.L.M.; Páscoa, R.N.M.J. Comparison of near Infrared Spectroscopy and Raman Spectroscopy for the Identification and Quantification through MCR-ALS and PLS of Peanut Oil Adulterants. Talanta 2021, 230, 122373.
Castro, R.C.; Ribeiro, D.S.M.; Santos, J.L.M.; Páscoa, R.N.M.J. Near Infrared Spectroscopy Coupled to MCR-ALS for the Identification and Quantification of Saffron Adulterants: Application to Complex Mixtures. Food Control 2021, 123, 107776.
Li, S.; Xing, B.; Lin, D.; Yi, H.; Shao, Q. Rapid Detection of Saffron (Crocus sativus L.) Adulterated with Lotus Stamens and Corn Stigmas by near-Infrared Spectroscopy and Chemometrics. Ind. Crops. Prod. 2020, 152, 112539.
Mehrnia, M.-A.; Jafari, S.-M.; Makhmal-Zadeh, B.S.; Maghsoudlou, Y. Rheological and Release Properties of Double Nano-Emulsions Containing Crocin Prepared with Angum Gum, Arabic Gum and Whey Protein. Food Hydrocoll. 2017, 66, 259–267.
Li, S.; Shao, Q.; Lu, Z.; Duan, C.; Yi, H.; Su, L. Rapid Determination of Crocins in Saffron by Near-Infrared Spectroscopy Combined with Chemometric Techniques. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2018, 190, 283–289.
Liang, P.-S.; Haff, R.P.; Hua, S.-S.T.; Munyaneza, J.E.; Mustafa, T.; Sarreal, S.B.L. Nondestructive Detection of Zebra Chip Disease in Potatoes Using Near-Infrared Spectroscopy. Biosyst. Eng. 2018, 166, 161–169.
De Girolamo, A.; von Holst, C.; Cortese, M.; Cervellieri, S.; Pascale, M.; Longobardi, F.; Catucci, L.; Porricelli, A.C.R.; Lippolis, V. Rapid Screening of Ochratoxin A in Wheat by Infrared Spectroscopy. Food Chem. 2019, 282, 95–100.
Liu, P.; Wang, J.; Li, Q.; Gao, J.; Tan, X.; Bian, X. Rapid Identification and Quantification of Panax Notoginseng with Its Adulterants by near Infrared Spectroscopy Combined with Chemometrics. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2019, 206, 23–30.
Miao, X.; Miao, Y.; Tao, S.; Liu, D.; Chen, Z.; Wang, J.; Huang, W.; Yu, Y. Classification of Rice Based on Storage Time by Using near Infrared Spectroscopy and Chemometric Methods. Microchem. J. 2021, 171, 106841.
Xie, L.-H.; Tang, S.-Q.; Wei, X.-J.; Sheng, Z.-H.; Shao, G.-N.; Jiao, G.-A.; Hu, S.-K.; Wang-Lin; Hu, P.-S. Simultaneous Determination of Apparent Amylose, Amylose and Amylopectin Content and Classification of Waxy Rice Using near-Infrared Spectroscopy (NIRS). Food Chem. 2022, 388, 132944.
Joshi, R.; Baek, I.; Joshi, R.; Kim, M.S.; Cho, B.-K. Detection of Fabricated Eggs Using Fourier Transform Infrared (FT-IR) Spectroscopy Coupled with Multivariate Classification Techniques. Infrared Phys. Technol. 2022, 123, 104163.
Chen, H.; Tan, C.; Lin, Z. Non-Destructive Identification of Native Egg by near-Infrared Spectroscopy and Data Driven-Based Class-Modeling. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2019, 206, 484–490.
Cai, C.; Huang, J.; Zhao, L.; Liu, Q.; Zhang, C.; Wei, C. Heterogeneous Structure and Spatial Distribution in Endosperm of High-Amylose Rice Starch Granules with Different Morphologies. J. Agric. Food Chem. 2014, 62, 10143–10152.
Jääskeläinen, A.-S.; Holopainen-Mantila, U.; Tamminen, T.; Vuorinen, T. Endosperm and Aleurone Cell Structure in Barley and Wheat as Studied by Optical and Raman Microscopy. J. Cereal Sci. 2013, 57, 543–550.
Piot, O.; Autran, J.-C.; Manfait, M. Spatial Distribution of Protein and Phenolic Constituents in Wheat Grain as Probed by Confocal Raman Microspectroscopy. J. Cereal Sci. 2000, 32, 57–71.
Nakajima, S.; Kuroki, S.; Ikehata, A. Selective Detection of Starch in Banana Fruit with Raman Spectroscopy. Food Chem. 2023, 401, 134166.
Czaja, T.; Mazurek, S.; Szostak, R. Quantification of Gluten in Wheat Flour by FT-Raman Spectroscopy. Food Chem. 2016, 211, 560–563.
Hara, R.; Ishigaki, M.; Kitahama, Y.; Ozaki, Y.; Genkawa, T. Excitation Wavelength Selection for Quantitative Analysis of Carotenoids in Tomatoes Using Raman Spectroscopy. Food Chem. 2018, 258, 308–313.
Tay, M.; Fang, G.; Chia, P.L.; Li, S.F.Y. Rapid Screening for Detection and Differentiation of Detergent Powder Adulteration in Infant Milk Formula by LC–MS. Forensic Sci. Int. 2013, 232, 32–39.
de Oliveira Mendes, T.; Manzolli Rodrigues, B.V.; Simas Porto, B.L.; Alves da Rocha, R.; de Oliveira, M.A.L.; de Castro, F.K.; dos Anjos, V.d.C.; Bell, M.J.V. Raman Spectroscopy as a Fast Tool for Whey Quantification in Raw Milk. Vib. Spectrosc. 2020, 111, 103150.
Czaja, T.; Baranowska, M.; Mazurek, S.; Szostak, R. Determination of Nutritional Parameters of Yoghurts by FT Raman Spectroscopy. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2018, 196, 413–417.
Tian, H.; Chen, S.; Li, D.; Lou, X.; Chen, C.; Yu, H. Simultaneous Detection for Adulterations of Maltodextrin, Sodium Carbonate, and Whey in Raw Milk Using Raman Spectroscopy and Chemometrics. J. Dairy. Sci. 2022, 105, 7242–7252.
Taylan, O.; Cebi, N.; Tahsin Yilmaz, M.; Sagdic, O.; Bakhsh, A.A. Detection of Lard in Butter Using Raman Spectroscopy Combined with Chemometrics. Food Chem. 2020, 332, 127344.
Richardson, P.I.C.; Muhamadali, H.; Ellis, D.I.; Goodacre, R. Rapid Quantification of the Adulteration of Fresh Coconut Water by Dilution and Sugars Using Raman Spectroscopy and Chemometrics. Food Chem. 2019, 272, 157–164.
Jiang, L.; Mehedi Hassan, M.; Jiao, T.; Li, H.; Chen, Q. Rapid Detection of Chlorpyrifos Residue in Rice Using Surface-Enhanced Raman Scattering Coupled with Chemometric Algorithm. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 261, 119996.
Robert, C.; Fraser-Miller, S.J.; Jessep, W.T.; Bain, W.E.; Hicks, T.M.; Ward, J.F.; Craigie, C.R.; Loeffen, M.; Gordon, K.C. Rapid Discrimination of Intact Beef, Venison and Lamb Meat Using Raman Spectroscopy. Food Chem. 2021, 343, 128441.
Yuan, H.; Liu, M.; Huang, S.; Zhao, J.; Tao, J. Classification and Detection of Testosterone Propionate and Nandrolone Residues in Duck Meat Using Surface-Enhanced Raman Spectroscopy Coupled with Multivariate Analysis. Poult. Sci. 2021, 100, 296–301.
Wang, Q.; Lonergan, S.M.; Yu, C. Rapid Determination of Pork Sensory Quality Using Raman Spectroscopy. Meat Sci. 2012, 91, 232–239.
Ning, X.; Mu-Hua, L.; Hai-Chao, Y.; Shuang-Gen, H.; Xiao, W.; Jin-Hui, Z.; Jian, C.; Ting, W.; Wei, H.; Yi-Xin, S. Classification of Sulfadimidine and Sulfapyridine in Duck Meat by Surface Enhanced Raman Spectroscopy Combined with Principal Component Analysis and Support Vector Machine. Anal. Lett. 2020, 53, 1513–1524.
Fowler, S.M.; Hopkins, D.L.; Torley, P.J.; Gill, H.; Blanch, E.W. Investigation of Chemical Composition of Meat Using Spatially Off-Set Raman Spectroscopy. Analyst 2019, 144, 2618–2627.
Tian, F.; Tan, F.; Li, H. An Rapid Nondestructive Testing Method for Distinguishing Rice Producing Areas Based on Raman Spectroscopy and Support Vector Machine. Vib. Spectrosc. 2020, 107, 103017.
Amjad, A.; Ullah, R.; Khan, S.; Bilal, M.; Khan, A. Raman Spectroscopy Based Analysis of Milk Using Random Forest Classification. Vib. Spectrosc. 2018, 99, 124–129.
Karunathilaka, S.R.; Yakes, B.J.; He, K.; Brückner, L.; Mossoba, M.M. First Use of Handheld Raman Spectroscopic Devices and On-Board Chemometric Analysis for the Detection of Milk Powder Adulteration. Food Control 2018, 92, 137–146.
de Sá Oliveira, K.; de Souza Callegaro, L.; Stephani, R.; Almeida, M.R.; de Oliveira, L.F.C. Analysis of Spreadable Cheese by Raman Spectroscopy and Chemometric Tools. Food Chem. 2016, 194, 441–446.
Aykas, D.P.; Shotts, M.-L.; Rodriguez-Saona, L.E. Authentication of Commercial Honeys Based on Raman Fingerprinting and Pattern Recognition Analysis. Food Control 2020, 117, 107346.
Kelis Cardoso, V.G.; Poppi, R.J. Cleaner and Faster Method to Detect Adulteration in Cassava Starch Using Raman Spectroscopy and One-Class Support Vector Machine. Food Control 2021, 125, 107917.
Figueiredo, L.P.; Borém, F.M.; Almeida, M.R.; de Oliveira, L.F.C.; de Carvalho Alves, A.P.; dos Santos, C.M.; Rios, P.A. Raman Spectroscopy for the Differentiation of Arabic Coffee Genotypes. Food Chem. 2019, 288, 262–267.
Jiménez-Carvelo, A.M.; Osorio, M.T.; Koidis, A.; González-Casado, A.; Cuadros-Rodríguez, L. Chemometric Classification and Quantification of Olive Oil in Blends with Any Edible Vegetable Oils Using FTIR-ATR and Raman Spectroscopy. LWT 2017, 86, 174–184.
Jin, H.; Li, H.; Yin, Z.; Zhu, Y.; Lu, A.; Zhao, D.; Li, C. Application of Raman Spectroscopy in the Rapid Detection of Waste Cooking Oil. Food Chem. 2021, 362, 130191.
Trimigno, A.; Marincola, F.C.; Dellarosa, N.; Picone, G.; Laghi, L. Definition of Food Quality by NMR-Based Foodomics. Curr. Opin. Food Sci. 2015, 4, 99–104.
Haddad, L.; Francis, J.; Rizk, T.; Akoka, S.; Remaud, G.S.; Bejjani, J. Cheese Characterization and Authentication through Lipid Biomarkers Obtained by High-Resolution 1H NMR Profiling. Food Chem. 2022, 383, 132434.
Hajjar, G.; Haddad, L.; Rizk, T.; Akoka, S.; Bejjani, J. High-Resolution 1H NMR Profiling of Triacylglycerols as a Tool for Authentication of Food from Animal Origin: Application to Hen Egg Matrix. Food Chem. 2021, 360, 130056.
Shi, T.; Zhu, M.; Chen, Y.; Yan, X.; Chen, Q.; Wu, X.; Lin, J.; Xie, M. 1H NMR Combined with Chemometrics for the Rapid Detection of Adulteration in Camellia Oils. Food Chem. 2018, 242, 308–315.
Santos, P.M.; Pereira-Filho, E.R.; Colnago, L.A. Detection and Quantification of Milk Adulteration Using Time Domain Nuclear Magnetic Resonance (TD-NMR). Microchem. J. 2016, 124, 15–19.
Sun, Q.; Zhang, M.; Yang, P. Combination of LF-NMR and BP-ANN to Monitor Water States of Typical Fruits and Vegetables during Microwave Vacuum Drying. LWT 2019, 116, 108548.
Milani, M.I.; Rossini, E.L.; Catelani, T.A.; Pezza, L.; Toci, A.T.; Pezza, H.R. Authentication of Roasted and Ground Coffee Samples Containing Multiple Adulterants Using NMR and a Chemometric Approach. Food Control 2020, 112, 107104.
Consonni, R.; Polla, D.; Cagliani, L.R. Organic and Conventional Coffee Differentiation by NMR Spectroscopy. Food Control 2018, 94, 284–288.
Marseglia, A.; Acquotti, D.; Consonni, R.; Cagliani, L.R.; Palla, G.; Caligiani, A. HR MAS 1H NMR and Chemometrics as Useful Tool to Assess the Geographical Origin of Cocoa Beans–Comparison with HR 1H NMR. Food Res. Int. 2016, 85, 273–281.
Zhang, Y.; Zhao, Y.; Shen, G.; Zhong, S.; Feng, J. NMR Spectroscopy in Conjugation with Multivariate Statistical Analysis for Distinguishing Plant Origin of Edible Oils. J. Food Compos. Anal. 2018, 69, 140–148.
Botoran, O.R.; Ionete, R.E.; Miricioiu, M.G.; Costinel, D.; Radu, G.L.; Popescu, R. Amino Acid Profile of Fruits as Potential Fingerprints of Varietal Origin. Molecules 2019, 24, 4500.
Rachineni, K.; Rao Kakita, V.M.; Awasthi, N.P.; Shirke, V.S.; Hosur, R.V.; Chandra Shukla, S. Identifying Type of Sugar Adulterants in Honey: Combined Application of NMR Spectroscopy and Supervised Machine Learning Classification. Curr. Res. Food Sci. 2022, 5, 272–277.
Cavdaroglu, C.; Ozen, B. Prediction of Vinegar Processing Parameters with Chemometric Modelling of Spectroscopic Data. Microchem. J. 2021, 171, 106886.
Tarhan, İ. A Comparative Study of ATR-FTIR, UV–Visible and Fluorescence Spectroscopy Combined with Chemometrics for Quantification of Squalene in Extra Virgin Olive Oils. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2020, 241, 118714.
Wu, X.; Bian, X.; Lin, E.; Wang, H.; Guo, Y.; Tan, X. Weighted Multiscale Support Vector Regression for Fast Quantification of Vegetable Oils in Edible Blend Oil by Ultraviolet-Visible Spectroscopy. Food Chem. 2021, 342, 128245.
Zhang, W.; Li, N.; Feng, Y.; Su, S.; Li, T.; Liang, B. A Unique Quantitative Method of Acid Value of Edible Oils and Studying the Impact of Heating on Edible Oils by UV–Vis Spectrometry. Food Chem. 2015, 185, 326–332.
Ríos-Reina, R.; Azcarate, S.M.; Camiña, J.; Callejón, R.M. Assessment of UV–Visible Spectroscopy as a Useful Tool for Determining Grape-Must Caramel in High-Quality Wine and Balsamic Vinegars. Food Chem. 2020, 323, 126792.
Gonçalves, T.R.; Rosa, L.N.; Gonçalves, R.P.; Torquato, A.S.; Março, P.H.; Marques Gomes, S.T.; Matsushita, M.; Valderrama, P. Monitoring the Oxidative Stability of Monovarietal Extra Virgin Olive Oils by UV–Vis Spectroscopy and MCR–ALS. Food Anal. Methods 2018, 11, 1936–1943.
Kucharska-Ambrożej, K.; Martyna, A.; Karpińska, J.; Kiełtyka-Dadasiewicz, A.; Kubat-Sikorska, A. Quality Control of Mint Species Based on UV-VIS and FTIR Spectral Data Supported by Chemometric Tools. Food Control 2021, 129, 108228.
Suhandy, D.; Yulia, M. Peaberry Coffee Discrimination Using UV-Visible Spectroscopy Combined with SIMCA and PLS-DA. Int. J. Food Prop. 2017, 20, S331–S339.
Torrecilla, J.S.; Aroca-Santos, R.; Cancilla, J.C.; Matute, G. Linear and Non-Linear Modeling to Identify Vinegars in Blends through Spectroscopic Data. LWT-Food Sci. Technol. 2016, 65, 565–571.
Cavdaroglu, C.; Ozen, B. Detection of Vinegar Adulteration with Spirit Vinegar and Acetic Acid Using UV–Visible and Fourier Transform Infrared Spectroscopy. Food Chem. 2022, 379, 132150.
Ullah, R.; Khan, S.; Ali, H.; Bilal, M. Potentiality of Using Front Face Fluorescence Spectroscopy for Quantitative Analysis of Cow Milk Adulteration in Buffalo Milk. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2020, 225, 117518.
Barreto, M.C.; Braga, R.G.; Lemos, S.G.; Fragoso, W.D. Determination of Melamine in Milk by Fluorescence Spectroscopy and Second-Order Calibration. Food Chem. 2021, 364, 130407.
Uncu, O.; Ozen, B. A Comparative Study of Mid-Infrared, UV–Visible and Fluorescence Spectroscopy in Combination with Chemometrics for the Detection of Adulteration of Fresh Olive Oils with Old Olive Oils. Food Control 2019, 105, 209–218.
Gu, H.; Lv, R.; Huang, X.; Chen, Q.; Dong, Y. Rapid Quantitative Assessment of Lipid Oxidation in a Rapeseed Oil-in-Water (o/w) Emulsion by Three-Dimensional Fluorescence Spectroscopy. J. Food Compos. Anal. 2022, 114, 104762.
Yuan, L.; Meng, X.; Xin, K.; Ju, Y.; Zhang, Y.; Yin, C.; Hu, L. A Comparative Study on Classification of Edible Vegetable Oils by Infrared, near Infrared and Fluorescence Spectroscopy Combined with Chemometrics. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2022, 288, 122120.
Meng, X.; Yin, C.; Yuan, L.; Zhang, Y.; Ju, Y.; Xin, K.; Chen, W.; Lv, K.; Hu, L. Rapid Detection of Adulteration of Olive Oil with Soybean Oil Combined with Chemometrics by Fourier Transform Infrared, Visible-near-Infrared and Excitation-Emission Matrix Fluorescence Spectroscopy: A Comparative Study. Food Chem. 2023, 405, 134828.
Antônio, D.C.; de Assis, D.C.S.; Botelho, B.G.; Sena, M.M. Detection of Adulterations in a Valuable Brazilian Honey by Using Spectrofluorimetry and Multiway Classification. Food Chem. 2022, 370, 131064.
Fang, H.; Wu, H.-L.; Wang, T.; Long, W.-J.; Chen, A.-Q.; Ding, Y.-J.; Yu, R.-Q. Excitation-Emission Matrix Fluorescence Spectroscopy Coupled with Multi-Way Chemometric Techniques for Characterization and Classification of Chinese Lager Beers. Food Chem. 2021, 342, 128235.
Valinger, D.; Longin, L.; Grbeš, F.; Benković, M.; Jurina, T.; Gajdoš Kljusurić, J.; Jurinjak Tušek, A. Detection of Honey Adulteratio—The Potential of UV-VIS and NIR Spectroscopy Coupled with Multivariate Analysis. LWT 2021, 145, 111316.
Wang, X.; Esquerre, C.; Downey, G.; Henihan, L.; O’Callaghan, D.; O’Donnell, C. Development of Chemometric Models Using Vis-NIR and Raman Spectral Data Fusion for Assessment of Infant Formula Storage Temperature and Time. Innov. Food Sci. Emerg. Technol. 2021, 67, 102551.
Puertas, G.; Vázquez, M. Cholesterol Determination in Egg Yolk by UV-VIS-NIR Spectroscopy. Food Control 2019, 100, 262–268.
Liu, H.; Chen, Y.; Shi, C.; Yang, X.; Han, D. FT-IR and Raman Spectroscopy Data Fusion with Chemometrics for Simultaneous Determination of Chemical Quality Indices of Edible Oils during Thermal Oxidation. LWT 2020, 119, 108906.
Li, Y.; Huang, Y.; Xia, J.; Xiong, Y.; Min, S. Quantitative Analysis of Honey Adulteration by Spectrum Analysis Combined with Several High-Level Data Fusion Strategies. Vib. Spectrosc. 2020, 108, 103060.
Kandpal, L.M.; Mouazen, A.M.; Masithoh, R.E.; Mishra, P.; Lohumi, S.; Cho, B.-K.; Lee, H. Sequential Data-Fusion of near-Infrared and Mid-Infrared Spectroscopy Data for Improved Prediction of Quality Traits in Tuber Flours. Infrared Phys. Technol. 2022, 127, 104371.
Yao, S.; Li, T.; Li, J.; Liu, H.; Wang, Y. Geographic Identification of Boletus Mushrooms by Data Fusion of FT-IR and UV Spectroscopies Combined with Multivariate Statistical Analysis. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2018, 198, 257–263.
Márquez, C.; López, M.I.; Ruisánchez, I.; Callao, M.P. FT-Raman and NIR Spectroscopy Data Fusion Strategy for Multivariate Qualitative Analysis of Food Fraud. Talanta 2016, 161, 80–86.
Huang, F.; Song, H.; Guo, L.; Guang, P.; Yang, X.; Li, L.; Zhao, H.; Yang, M. Detection of Adulteration in Chinese Honey Using NIR and ATR-FTIR Spectral Data Fusion. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2020, 235, 118297.
Wang, W.; Paliwal, J. Near-Infrared Spectroscopy and Imaging in Food Quality and Safety. Sens. Instrum. Food Qual. Saf. 2007, 1, 193–207.
Kademi, H.I.; Ulusoy, B.H.; Hecer, C. Applications of Miniaturized and Portable near Infrared Spectroscopy (NIRS) for Inspection and Control of Meat and Meat Products. Food Rev. Int. 2019, 35, 201–220.
McVey, C.; Elliott, C.T.; Cannavan, A.; Kelly, S.D.; Petchkongkaew, A.; Haughey, S.A. Portable Spectroscopy for High Throughput Food Authenticity Screening: Advancements in Technology and Integration into Digital Traceability Systems. Trends Food Sci. Technol. 2021, 118, 777–790.
Herrero, A.M. Raman Spectroscopy a Promising Technique for Quality Assessment of Meat and Fish: A Review. Food Chem. 2008, 107, 1642–1651.
Özbalci, B.; Boyaci, İ.H.; Topcu, A.; Kadılar, C.; Tamer, U. Rapid Analysis of Sugars in Honey by Processing Raman Spectrum Using Chemometric Methods and Artificial Neural Networks. Food Chem. 2013, 136, 1444–1452.
Prieto, N.; Dugan, M.E.R.; Juárez, M.; López-Campos, Ó.; Zijlstra, R.T.; Aalhus, J.L. Using Portable Near-Infrared Spectroscopy to Predict Pig Subcutaneous Fat Composition and Iodine Value. Can. J. Anim. Sci. 2017, 98, 221–229.
Mishra, P.; Sytsma, M.; Chauhan, A.; Polder, G.; Pekkeriet, E. All-in-One: A Spectral Imaging Laboratory System for Standardised Automated Image Acquisition and Real-Time Spectral Model Deployment. Anal. Chim. Acta 2022, 1190, 339235.
Mishra, P.; Nordon, A.; Asaari, M.S.M.; Lian, G.; Redfern, S. Fusing Spectral and Textural Information in Near-Infrared Hyperspectral Imaging to Improve Green Tea Classification Modelling. J. Food Eng. 2019, 249, 40–47.
Bwambok, D.K.; Siraj, N.; Macchi, S.; Larm, N.E.; Baker, G.A.; Pérez, R.L.; Ayala, C.E.; Walgama, C.; Pollard, D.; Rodriguez, J.D. QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs. Sensors 2020, 20, 6982.
Beć, K.B.; Grabska, J.; Huck, C.W. Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives. Foods 2022, 11, 1465.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Spectroscopy

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Mourad Kharbach

Mohammed Alaoui Mansouri

Mohammed Taabouz

Huiwen Yu

View Times: 476

Update Date: 31 Jul 2023

Table of Contents

Video Upload Options

Confirm