Near-Infrared Hyperspectral Imaging Techniques for Non-Destructive Quality Assessment: History
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Subjects: Spectroscopy
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Hyperspectral imaging (HSI) is one of the most often used techniques for rapid quality evaluation for various applications. It is a non-destructive technique that effectively evaluates the quality attributes of root and tuber crops, including yam and cassava, and their food products. Hyperspectral imaging technology, which combines spectroscopy and imaging principles, has an advantage over conventional spectroscopy due to its ability to simultaneously evaluate the physical characteristics and chemical components of various food products and specify their spatial distributions. HSI has demonstrated significant potential for obtaining quick information regarding the chemical composition of the root and tuber, such as starch, protein, dry matter, amylose, and soluble sugars, as well as physical characteristics such as textural properties and water binding capacity.

  • hyperspectral imaging
  • spectroscopy
  • quality evaluation
  • yam
  • cassava
  • image processing

1. Introduction

The world’s tropics and subtropics depend on root and tuber crops such as cassava, yam, potato, and sweet potato as critical staple foods that are consumed in various ways [1]. Additionally, they serve as the starting point for small-scale industrial production, particularly in underdeveloped nations [2]. Nearly 700 million people in subtropical and tropical areas mainly obtain carbohydrates and energy from cassava (Manihot esculenta Crantz) root [3]. The leaves also supply protein, vitamins, and minerals [4]. The roots have a dry matter composition of 80 to 90% carbohydrates, of which 80% is starch and the other minute amounts are sucrose, glucose, fructose, and maltose. They also contain about 0.1– 0.5% fat, 1–3% protein, and 80–90% carbohydrates, respectively. Yam (Dioscorea spp.) is another staple crop cultivated in Africa, Asia, South America, the Caribbean, and the South Pacific [5]. Generally, it provides energy ranging between 80 and 120 kcal/100 g, depending on the variety. The moisture content of fresh tubers ranges between 58 and 80%, 0.5–1.2% for ash, 17.5–28% for carbohydrates, 1.5–6% for crude protein, 0.1–0.2% for fat, and 0.6–1.5% for fiber, on a wet basis [3]. Cassava and yam breeding programs need to evaluate many genotypes for agronomic parameters, nutritional composition, and end users’ preferred attributes to facilitate the breeding of crops with top-performance quality and increase adoption by farmers and processors. However, evaluating these traits is cumbersome, as it is costly and time-consuming, especially when using conventional approaches. Therefore, this brings to the fore the necessity to provide a cost-effective, time-saving, and accurate prediction of those important traits to make informed decisions in the selection process, especially in large breeding populations.
Near-infrared spectroscopy (NIRS) has proven to be a reliable tool for predicting various quality parameters in many breeding programs, such as cassava, yam, potato, and sweet potato [1,6,7,8,9,10,11]. Its application in breeding programs has improved, as it enhances the adoption of modern NIRS and has been used to accurately predict key quality traits, as reported by many authors on genetic technologies that require the phenotyping of many clones for complex features within the shortest time possible and at minimal cost, especially at the early breeding stages [8]. NIRS has been used to evaluate many quality traits in crops, and their flour, with a good to a medium coefficient of prediction, as reported in [1,12,13,14,15,16,17].
To highlight the potential of NIRS for the investigation of numerous chemical constituents, Alamu et al. [18] wrote a review which shows that NIRS has potential as a high-throughput phenotyping tool for root and tuber crops. Additionally, their research showed that most published studies supported the ability of NIRS to accurately predict biochemical parameters such as starch, soluble sugar, and many others. However, there are only a few studies confirming the possibility of NIRS predicting other quality attributes related to end-product quality, which inform consumers’ preferences. These are seemingly complex traits because the quality of the product has been impacted by processing factors [18]. However, the emergence of near-infrared hyperspectral imaging (NIR-HSI) represents a new development in the application of spectroscopy. By combining the spatial and spectral data of the target sample, this method merges imaging and spectroscopic concepts with the ability to capture additional inherent information about the product. It can accurately predict the biochemical properties, physical (internal and external) features, and spatial information of the chemical components in the products [19]. It has gained broad interest in the noninvasive quality monitoring of many food crops [19].
NIR-HSI was originally developed for remote sensing applications, but it is now used to facilitate complete and reliable analyses of the inherent physical and chemical properties of food products [20]. Recently, many authors have reported using NIR-HSI to assess quality attributes in food and other products [21,22,23,24]. In addition, it has been extensively used for the physical and biochemical constituent characterization of potatoes and sweet potatoes [25,26,27,28,29,30].

2. Overview of Near-Infrared Reflectance Spectroscopy (NIRS)

NIRS is a fast, non-destructive analytical technique widely used to analyze organic constituents and other properties in various agricultural products with minimum or no sample preparation steps [37]. It employs a wavelength between 780 nm (12,500 cm−1) and 2500 nm (14,000 cm−1), providing more complex structural information about bond vibration behavior. Chemical bonds between light atoms in molecules such as C, N, O, and H with primary absorption in the infrared (IR) region have strong vibrational overtones and combination bands that absorb light in the near-infrared (NIR) region (780–2500 nm) of electro-magnetic radiation. The NIRS region in the electromagnetic radiation has a linear relationship between the absorbance and concentration (i.e., the Beer–Lambert–Bouguerre relationship), making it an important analytical tool [38]. The Beer–Lambert–Bouguerre relationship is a main rule in spectrophotometric analysis that gives a great opportunity to find the concentration of a substance by measuring the absorbance of its solution [38].
Organic molecules absorb energy in the near-infrared region when they vibrate or are translated into an absorption spectrum within an NIR spectrometer. At 1300 nm, the NIR region is divided into short-wave NIR (SW-NIR) in the wavelength range of 700 to 1100 nm and standard NIR (780 to 2500 nm). The SW-NIR region is an absorption band of high overtones, whereas the traditional NIR region is an absorption band of the first or second overtone [39,40]. The intensity of absorption decreases as the overtones increase. As a result, SW-NIR is frequently used in transmission analysis, where reflection is significantly reduced so that the amount of radiation attenuated by the sample is measured in transmittance modes, as opposed to standard NIR, which is used in diffuse reflection analysis, which is frequently used for the analysis of opaque solids and is associated with light scattering at the surface to obtain surface information of the samples [41]. Because of the interaction of electromagnetic radiation in the near-infrared region and biological tissues, NIRS has found widespread application in the quantitative evaluation of various crops.
NIRS could be useful for qualitative measurement, but due to the overlapping and non-specific nature of NIR spectra, they become difficult to interpret. However, each peak has enormous hidden information of the molecular bonds absorbing in the respective wavelengths. NIR absorptions between 700 and 1050 nm are usually the second and third overtones of both C-H and O-H bonds, which are mainly for starch and water. Oil has a unique absorption band which appears as a duplet at two characteristic wavelengths of 1700 nm and 2300 nm, while water absorption is at 1925 nm, which is indicative of stretching and bending vibrations of O-H [41]. The combination band of NH at 2130 and 2190 nm is indicative of protein, whereas the first overtones region was the best for predicting starch (1452–1770 nm) [41].

3. Overview of Hyperspectral Imaging Spectroscopy (HSI)

HSI is a modern method which incorporates the critical concept of imaging and spectroscopy and can concurrently obtain spectral and image information from a sample [48,49,50,51,52,53,54]. NIR-HIS was initially used in remote sensing studies but now serves as an emerging technology in various quantitative applications in the food [31,55,56,57], medicine, and agriculture industries [58,59,60]. HSI is a promising method for the rapid and nondestructive sorting and prediction of quality parameters in various root and tuber crop categories, including yam and cassava [21]. NIR-HSI systems can capture a broad range of spectra data from visible to near-infrared and far-infrared regions of electromagnetic radiation. The pixel in the NIR-HSI image has a continuous spectrum of about a hundred bands [61,62,63].
Additionally, the image contains valuable information on the intrinsic chemical compositions and their spatial distributions within the target. HSI has shown the potential to characterize the biochemical and biophysical constituents, including their spatial distribution, simultaneously. Spectral imaging technology is classified as multispectral, hyperspectral, or ultraspectral [19,64].
Hyperspectral images can be generated in various ways, which include a tunable filter, push broom, and whiskbroom, respectively [64]; this depends on the hardware used for the data acquisition. A tunable filter keeps the target fixed and obtains images subsequently from one wavelength to another; this is used when the number of wavelengths needed is limited. ElMasry and Nakauchi, [19] stated that a push broom and whiskbroom rely on scanning the target in the spatial domain by moving the target either line-by-line (push broom) or point-by-point (whiskbroom). Additionally, HSI can be operated in different optical modes, such as reflectance, transmittance, absorbance, or fluorescence, depending on the optical properties of the samples. Most of the published work was performed in the reflectance mode [65,66,67,68]. A “hypercube” is a three-dimensional (3-D) structure obtained with HSI that consists of two spatial and one spectral dimension [69,70]. Because of their ability to combine conventional imaging and spectroscopy, HSI systems can provide physical and geometrical features of the target (i.e., color and appearance) and the chemical composition. As a result, hyperspectral imaging technology has distinct advantages in detecting plant materials’ outward and intrinsic quality. It has numerous advantages over traditional analytical methods, including the nondestructive nature of samples and the unrivalled prediction accuracy. It can quickly determine the chemical composition of foods and the spatial distribution of the quality attributes (69).

4. NIR-Hyperspectral Imaging Spectroscopy for Yam and Cassava Food Quality

NIR spectroscopy has recently moved from traditional spectroscopy to coupling with other technologies, including NIR-microscopy, NIR-MIR Spectroscopy, and NIR-Hyperspectral imaging spectroscopy for the quality assessment of root and tuber crops. Along with increased spectra quality from the millions of spectral data points acquired at each wavelength, NIR-HSI also gives information on the spatial distribution of the target product’s chemical components. Numerous root and tuber crops, particularly potatoes and sweet potatoes, have been reported to use NIR-HSI for their food quality assessment [26,75,76,77,78,79,80,81,82].
Alamu et al. [18] mentioned in their review paper that only one work characterizing cassava by applying NIR-HIS, that of Su and Sun [83], had been reported at the time of their research. The authors employed the HSI method to identify the adulteration of cassava flour in Irish organic wheat flour (OWF). Between 900 and 1700 nm, hyperspectral images were taken using OWF samples that had different levels of percentage adulterations. For quantitative analysis, PLSR and principal component regression (PCR) were used, and feature wavelengths were chosen using the first derivative and mean centering iteration procedure using the loading plots of PCA (FMCIA). Wavelengths were further decreased following the corresponding feature using the model regression coefficients (RC). The RC-FMCIA-PLSR model produced the best admixture detection outcome for OWF mixed with cassava flour, with R2P = 0.973 and RMSEP = 0.036. Khamsopha et al. [36] later reported on another use of NIR-HSI for identifying adulterations of cassava flour in tapioca starch. This investigation added limestone powder to tapioca starch at intervals of 0.5% across a range of 0–100% (wt/wt) to create adulterated tapioca starch. A calibration set of samples (N = 141) and a prediction set of samples (N = 61) were used in the study. All samples were scanned with the NIR-HSI equipment at a wavelength of 935–1720 nm. The model’s prediction accuracy was perfect, with a correlation coefficient (R) of 0.99 and a root mean square error of prediction (RMSEP) of 2.47%. Using prediction model visualization techniques, the study demonstrated the potential of NIR-HSI as a quick way for identifying the levels of adulteration in tapioca starch. The application of NIR-HSI for dolomite adulteration in tapioca starch was also evaluated in a different study by the same researchers, who added dolomite in concentrations ranging from 0.5 to 100% (wt/wt). Using NIR-HSI at 935 to 1720 nm, 400 samples of pure and contaminated tapioca starch were scanned. These samples were separated into a calibration set (N = 300) and a validation set (N = 100). For preprocessing, Savitzky–Golay’s first derivative differentiation was utilized to create the ideal environment for the classification model. The model’s classification of pure and contaminated tapioca was assessed to be 100% accurate [84]. Although there are many practical applications using NIR-HSI for other root and tuber crops, especially potato and sweet potatoes, only a few studies were reported on using NIR-HSI for quality characterization of cassava and yam tubers. A standard operating procedure (SOP) for monitoring water distribution in fresh yam using HSI was reported in the framework of the RTBfoods project. However, this SOP only described the use of HSI to detect the longitudinal distribution of water in fresh roots and tubers using multivariate analysis [85]. Therefore, further SOPs must be developed to investigate the cross-sectional parts of the root and tuber crop for physical and chemical characteristics.

5. Quality Evaluation of Potatoes and Sweet Potatoes with NIR-Hyperspectral Imaging Techniques

The water content and weight of potato tubers was assessed using the hyperspectral imaging technique and artificial neural network algorithms, where 934–997 nm was the wavelength range found to be selective for the absorption band in predicting the water content in the potato tuber [85]. Measurements of the dry matter of potato and sweet potato were conducted using hyperspectral imaging in conjunction with LWPLSR, PLSR, and MLR. Using the MLR model, a highly satisfactory prediction coefficient (R2P) of 0.96 was obtained [86]. A multispectral real-time system was developed to monitor the moisture content (MC) in dried potato and sweet potato products using near-infrared (NIR) and mid-infrared (MIR) hyperspectral techniques combined with chemometric algorithms. Multivariate models were created using partial least squares regression (PLSR), support vector machine regression (SVMR), locally weighted partial least square regression (LWPLSR), and a back propagation artificial neural network (BPANN) in the full spectral range of 900–10,372 cm−1. The prediction (R2P) determination coefficients of 0.950 and 0.904, respectively, were obtained from the simplified SPA-LWPLSR and SPA-BPANN, respectively, indicating good model performances for the tuber MC prediction [87]. Additionally, the NIR hyperspectral technology was used to predict the starch content of sweet potato and potato [88]. The feasibility of hyperspectral imaging systems in monitoring the changes in the moisture (MC) and total anthocyanin (TA) contents of purple sweet potatoes [PSP] during convective hot air drying (CHD) and microwave drying (MD) was investigated [80]. The PLSR model was developed after spectra extraction to predict the TA and MC contents of the processed purple sweet potatoes. For the CHD, a determination coefficient in prediction (R2p) of 0.836 and 0.817 and a root mean square error (RMSEP) of 0.091 and 0.407 were reported for MC and TA, respectively. However, the R2p obtained for the MD was 0.831 and 0.766, with an RMSEP of 0.095 and 0.382 for MC and TA, respectively. The authors also established that HSI could be useful for visualizing the distribution of MC and TA during the drying process of the purple sweet potatoes. The authors observed a uniform distribution of MC and TA at the initial drying stage by CHD until after 45 min of drying, when high moisture loss was observed from the core of the sample. They reported that convective hot air drying has better distribution uniformity of the measured parameters than the microwave drying [80]. The starch contents of fresh-cut potatoes were analyzed with hyperspectral imaging techniques using Competitive Adaptive Reweighed Sampling (CARS) and the successive projection algorithm (SPA) to extract characteristic wavelengths from the images. A PLSR model was developed to predict the starch content from the preprocessed full spectrum and the spectrum under the characteristic wavelength. The results indicate that the full spectrum model constructed through standard normal variable transformation (SNV) had the best performance, with a correlation coefficient in the calibration set (Rc) value of 0.9020, a root mean square error of correction (RMSEC) of 2.06, and a residual prediction deviation (RPD) of 2.33 [89].

6.Physical Parameters and Texture Analysis Using Hyperspectral Imaging

Physical parameters such as the color and textural attributes of roots and tubers have become an essential factor driving their final quality at a consumption stage. Consumers’ preferences for product quality are influenced mainly by color, particularly when processing substantially impacts product quality [108]. Xiao et al. [109] reported that NIR-HSI was used to determine the color of potatoes. The textural attributes of cassava and yam products, such as boiled and pounded forms, are determined by a sensory evaluation, which may be a subjective and mechanical instrument measurement which requires considerable time [108]. Hyperspectral imaging has been used in evaluating the color and other physical characteristics of other tuber crops, such as potatoes and sweet potatoes [74,106,107,109]. The color of potato slices was observed as they were being air dried using Vis/NIR hyperspectral imaging, and the R2P was as high as 0.91 when the PLSR was paired with feature wavelength selection techniques such as chosen interval partial least squares regression (iPLSR) [110]. PLSR was also used to determine the specific gravity and water absorption of sliced potatoes using hyperspectral imaging systems in the NIR spectra range of 900–1700 nm. With the linear weighted principal component regression algorithm, a coefficient of prediction (R2p) of 0.98 was obtained for specific gravity, and one of 0.97 was obtained for water absorption capacity [97]. The textural characteristics of potatoes and sweet potatoes were assessed during microwave baking using the MIR spectra (600–4000 cm−1); in this research, the LWPLSR performed better than PLSR in determining associated textural qualities such as chewiness, resilience, hardness, gumminess, cohesiveness, and springiness, with a maximum R2P value of 0.88 [111]. However, limited literature using it for the textural qualities of cassava and yam exists. Hyperspectral image spectroscopy can potentially support the genetic improvement target for cassava and yam breeding programs by exploring intrinsic quality traits such as color, texture, and selected biochemical parameters. These influence the specific characteristics of root and tuber crops during processing and consumption, with the advantage of nondestructive sampling. NIR-HSI could be adopted as a high throughput method for assessing the food quality of cassava and yam food products. The conventional techniques for texture measurement are destructive to the samples and are sometimes influenced by human factors in the case of the sensory test [112]. However, the hyperspectral imaging technique has found applications in evaluating the textural attributes of potatoes and sweet potatoes [34,111].

7. Limitation of NIR-HSI Spectroscopy

Despite the importance of hyperspectral imaging techniques, it has certain limitations in its applications. NIR-HIS has a vast amount of data, including redundant information that poses challenges during data processing and computational analysis; such massive data require enough storage space for the computer, which adds to the cost of accessories. Second, like conventional spectroscopy, the accuracy of NIR-HSI, an indirect technique, depends on the standard of the reference values; hence, the prediction accuracies depend on the reliability of the wet laboratory analysis. Third, since the strength of imaging resides in its capacity to discern spatial heterogeneity in models, hyperspectral imaging is inappropriate for homogeneous materials such as liquid samples. In addition, providing samples with a high water content, such as fresh foods, results in a strong absorption band in a particular spectral area and obstructs the processing of spectra. Fourth, multicollinearity is another known limitation of hyperspectral imaging. In addition, image pre-processing and modeling could be time-consuming and affected by interferences from instrumental noise and other external factors, such as the ambient condition of the instrument room, which are sometimes challenging to control.

8. Prospects

In the future, researchers should develop more efficient algorithms for data processing and spectral band selection to solve the problem of high dimensionality. Reliable reference values must be obtained for targeted parameters because prediction performances rely on the quality of the reference values. It is imminent that more research on applying NIR-HSI techniques to define and characterize the critical quality parameters for yam and cassava should be conducted. It will contribute significantly to breeding programs to incorporate the priority quality characteristics influencing consumers’ decisions on adopting and utilizing pipeline varieties. Moreover, easy-to-use and accessible software for image processing should be available for research to enhance the handling and processing of spectra and image datasets.


  1. Abewoy, D. Review on postharvest handling practices of root and tuber crops. Int. J. Plant Breed. Crop Sci.2021, 8, 992–1000. [Google Scholar]
  2. Scott, G.J. A review of root, tuber and banana crops in developing countries: Past, present and future. Int. J. Food Sci. Technol. 2021, 56, 1093–1114. [Google Scholar] [CrossRef] [PubMed]
  3. Ferraro, V.; Piccirillo, C.; Tomlins, K.; Pintado, M.E. Cassava (Manihot esculenta Crantz) and Yam (Dioscoreaspp.) Crops and Their Derived Foodstuffs: Safety, Security and Nutritional Value. Crit. Rev. Food Sci. Nutr.2015, 56, 2714–2727. [Google Scholar] [CrossRef]
  4. Latif, S.; Müller, J. Potential of cassava leaves in human nutrition: A review. Trends Food Sci. Technol. 2015, 44, 147–158. [Google Scholar] [CrossRef]
  5. Obidiegwu, J.E.; Akpabio, E.M. The geography of yam cultivation in southern Nigeria: Exploring its social meanings and cultural functions. J. Ethn. Foods 2017, 4, 28–35. [Google Scholar] [CrossRef]
  6. Belalcazar, J.; Dufour, D.; Andersson, M.S.; Pizarro, M.; Luna, J.; Londoño, L.; Morante, N.; Jaramillo, A.M.; Pino, L.; López-Lavalle, L.A.B.; et al. High-Throughput Phenotyping and Improvements in Breeding Cassava for Increased Carotenoids in the Roots. Crop. Sci. 2016, 56, 2916–2925. [Google Scholar] [CrossRef]
  7. Ikeogu, U.N.; Davrieux, F.; Dufour, D.; Ceballos, H.; Egesi, C.N.; Jannink, J.-L. Rapid analyses of dry matter content and carotenoids in fresh cassava roots using a portable visible and near infrared spectrometer (Vis/NIRS). PLoS ONE 2017, 12, e0188918. [Google Scholar] [CrossRef]
  8. Sanchez, T.; Ceballos, H.; Dufour, D.; Ortiz, D.; Morante, N.; Calle, F.; Zum Felde, T.; Dominguez, M.; Davrieux, F. Prediction of carotenoids, cyanide, and dry matter contents in fresh cassava root using NIRS and Hunter colour techniques. Food Chem. 2014, 151, 444–451. [Google Scholar] [CrossRef]
  9. Lebot, V.; Malapa, R. Application of near infrared reflectance spectroscopy for the evaluation of yam (Dioscorea alata) germplasm and breeding lines. J. Sci. Food Agric. 2012, 93, 1788–1797. [Google Scholar] [CrossRef]
  10. Davrieux, F.; Dufour, D.; Dardenne, P.; Belalcazar, J.; Pizarro, M.; Luna, J.; Londoño, L.; Jaramillo, A.; Sanchez, T.; Morante, N.; et al. LOCAL regression algorithm improves near-infrared spec-troscopy predictions when the target constituent evolves in breeding populations. J. Near Infrared Spectrosc. 2016, 24, 109–117. [Google Scholar] [CrossRef]
  11. Phambu, N.; Meya, A.S.; Djantou, E.B.; Phambu, E.N.; Kita-Phambu, P.; Anovitz, L.M. Direct Detection of Residual Cyanide in Cassava Using Spectroscopic Techniques. J. Agric. Food Chem. 2007, 55, 10135–10140. [Google Scholar] [CrossRef] [PubMed]
  12. Alamu, E.O.; Maziya-Dixon, B.; Felde, T.Z.; Kulakow, P.; Parkes, E. Application of near-infrared reflectance spectroscopy in the screening of fresh cassava (Manihot esculenta Crantz) storage roots for provitamin A carotenoids. In Proceedings of the 18th International Conference of Near-Infrared Spectroscopy; Engelsen, S., Sørensen, K., Berg, F., Eds.; IMPublications Open: Chichester, UK, 2019; pp. 91–97. [Google Scholar] [CrossRef]
  13. Lu, G.Q.; Huang, H.H.; Zhang, D.P. Prediction of sweet potato starch physiochemical quality and pasting properties using near-infrared reflectance spectroscopy. Food Chem. 2006, 94, 632–639. [Google Scholar] [CrossRef]
  14. Hong, J.; Ikeda, K.; Kreft, I.; Yasumoto, K. Near-infrared diffuse reflectance spectroscopic analysis of the amounts of moisture, protein, starch, amylose, and tannin in buckwheat flours. J. Nutr. Sci. Vitaminol. 1996, 42, 359–366. [Google Scholar] [CrossRef]
  15. Katayama, K.; Komaki, K.; Tamiya, S. Prediction of starch, moisture, and sugar in sweet potato by near-infrared transmittance. Hortic. Sci. 1996, 31, 1003–1006. [Google Scholar] [CrossRef]
  16. Lebot, V.; Malapa, R.; Jung, M. Use of NIRS for the rapid prediction of total N, minerals, sugars and starch in tropical root and tuber crops. N. Z. J. Crop Hortic. Sci. 2013, 41, 144–153. [Google Scholar] [CrossRef]
  17. Adebayo, S.E.; Hashim, N.; Abdan, K.; Hanafi, M. Application and potential of back-scattering imaging techniques in agricultural and food processing—A review. J. Food Eng. 2016, 169, 155–164. [Google Scholar] [CrossRef]
  18. Alamu, E.O.; Nuwamanya, E.; Cornet, D.; Meghar, K.; Adesokan, M.; Tran, T.; Belalcazar, J.; Desfontaines, L.; Davrieux, F. Near-Infrared spectroscopy (NIRS) applications for high throughput phenotyping (HTP) for cassava and yam: A review. Int. J. Food Sci. Technol. 2021, 56, 1491–1501. [Google Scholar] [CrossRef]
  19. ElMasry, G.M.; Nakauchi, S. Image analysis operations applied to hyperspectral images for non-invasive sensing of food quality—A comprehensive review. Biosyst. Eng. 2016, 142, 53–82. [Google Scholar] [CrossRef]
  20. Mahesh, S.; Manickavasagan, A.; Jayas, D.S.; Paliwal, J.; Whiteb, N.D.G. Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosyst. Eng. 2008, 101, 50–57. [Google Scholar] [CrossRef]
  21. Li, L.; Zhang, Q.; Huang, D. A review of imaging techniques for plant phenotyping. Sensors 2014, 14, 20078–20111. [Google Scholar] [CrossRef]
  22. Manley, M. Near-infrared spectroscopy and hyperspectral imaging: Non-destructive analysis of biological materials. Chem. Soc. Rev. 2014, 43, 8200–8214. [Google Scholar] [CrossRef] [PubMed]
  23. Peirs, A.; Scheerlinck, N.; Nicolai, B.M. Temperature compensation for near infrared reflectance measurement of apple fruit soluble solids contents. Postharvest Biol. And. Technol. 2003, 30, 233–248. [Google Scholar] [CrossRef]
  24. ElMasry, G.; Sun, D.W. Principles of hyperspectral imaging technology. In Hyperspectral Imaging for Food Quality Analysis and Control; Academic Press: London, UK, 2010; pp. 3–43. [Google Scholar]
  25. Amjad, W.; Crichton SO, J.; Munir, A.; Hensel, O.; Sturm, B. Hyperspectral imaging for the determination of potato slice moisture content and chromaticity during the convective hot air-drying process. Biosyst. Eng. 2018, 166, 170–183. [Google Scholar] [CrossRef]
  26. Gerhard-Herman, M.D.; Gornik, H.L.; Barrett, C.; Barshes, R.N.; Corriere, M.A.; Drachman, D.E.; Fleisher, L.A.; Fowkes, F.G.R.; Hamburg, N.M.; Kinlay, S.; et al. AHA/ACC guideline on the management of patients with lower extremity peripheral artery disease: A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J. Am. Coll. Cardiol. 2016, 135, 726–779. [Google Scholar]
  27. Kjær, A.; Nielsen, G.; Stærke, S.; Clausen, M.R.; Edelenbos, M.; Jørgensen, B. Detection of Glycoalkaloids and Chlorophyll in Potatoes (Solanum tuberosum L.) by Hyperspectral Imaging. Am. J. Potato Res. 2017, 94, 573–582. [Google Scholar] [CrossRef]
  28. Do Trong, N.N.; Erkinbaev, C.; Nicolaï, B.; Saeys, W.; Tsuta, M.; De Baerdemaeker, J. Spatially resolved spectroscopy for nondestructive quality measurements of Braeburn apples cultivated in sub-fertilization condition. Sens. Technol. Biomat. Food Agric. 2013, 8881, 116–122. [Google Scholar]
  29. Su, W.H.; Sun, D.W. Advanced analysis of roots and tubers by hyperspectral techniques. Adv. Food Nutr. Res.2019, 87, 255–303. [Google Scholar]
  30. Su, W.-H.; Bakalis, S.; Sun, D.-W. Fourier transform mid-infrared-attenuated total reflectance (FTMIR-ATR) micro spectroscopy for determining a textural property of microwave baked tuber. J. Food Eng. 2017, 218, 1–13. [Google Scholar] [CrossRef]
  31. Liu, Z.Y.; Wu, H.F.; Huang, J.F. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Comput. Electron. Agric. 2010, 72, 99–106. [Google Scholar] [CrossRef]
  32. Williams, P.; Geladi, P.; Fox, G.; Manley, M. Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Anal. Chim. Acta 2009, 653, 121–130. [Google Scholar] [CrossRef]
  33. Rady, A.; Guyer, D.; Lu, R. Evaluation of Sugar Content of Potatoes using Hyperspectral Imaging. Food Bioprocess Technol. 2015, 8, 995–1010. [Google Scholar] [CrossRef]
  34. Su, W.H.; Bakalis, S.; Sun, D.W. Chemometrics in tandem with near-infrared (NIR) hyperspectral imaging and Fourier transform mid-infrared (FT-MIR) microspectroscopy for variety identification and cooking loss determination of sweet potato. Biosyst. Eng. 2019, 180, 70–86. [Google Scholar] [CrossRef]
  35. Su, W.-H.; Sun, D.-W. Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review. Compr. Rev. Food Sci. Food Saf. 2017, 17, 104–122. [Google Scholar] [CrossRef] [PubMed]
  36. Khamsopha, D.; Woranitta, S.; Teerachaichayut, S. Utilizing near-infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch. Food Control 2021, 123, 107781. [Google Scholar] [CrossRef]
  37. Bock, J.E.; Connelly, R.K. Innovative Uses of Near-Infrared Spectroscopy in Food Processing. J. Food Sci.2008, 73, R91–R98. [Google Scholar] [CrossRef] [PubMed]
  38. Badr, A. Near-infra-red Spectroscopy. In Wide Spectra of Quality Control; InTech: Rijeka, Croatia, 2011. [Google Scholar] [CrossRef]
  39. Rathmell, C.; Bingemann, D.; Zieg, M.; Creasey, D. Portable Raman Spectroscopy: Instrumentation and Technology. In Portable Spectroscopy and Spectrometry; Wiley: Hoboken, NJ, USA, 2021; pp. 115–145. [Google Scholar]
  40. Tsenkova, R.; Atanassova, S.; Toyoda, K. Near-infrared spectroscopy for diagnosis: Influence of mammary gland inflammation on cow’s milk composition measurement. Near Infrared Anal. 2001, 2, 59–66. [Google Scholar]
  41. Corson, D.C.; Waghorn, G.C.; Ulyatt, M.J.; Lee, J. NIRS: Forage analysis and livestock feeding. In Proceedings of the New Zealand Grassland Association; New Zealand Grassland Association; Wellington, New Zealand, 1999; Volume 61, pp. 127–132. Available online: (accessed on 17 December 2022).
  42. Osborne, B.G. Near-infrared spectroscopy in food analysis. In Encyclopedia of Analytical Chemistry; Meyers, R.A., Ed.; John Wiley & Sons: Chichester, UK, 2000; pp. 1–13. [Google Scholar]
  43. Restaino, E.A.; Fernández, E.G.; La Manna, A.; Cozzolino, D. Prediction of the nutritive value of pasture silage by near in-frared spectroscopy (Nirs). Chil. J. Agric. Resour. 2008, 69, 560–566. [Google Scholar]
  44. Huang, H.; Yu, H.; Xu, H.; Ying, Y. Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: A review. J. Food Eng. 2008, 87, 303–313. [Google Scholar] [CrossRef]
  45. Choudhary, R.; Mahesh, S.; Paliwal, J.; Jayas, D.S. Identification of wheat classes using wavelet features from near-infrared hyperspectral images of bulk samples. Biosyst. Eng. 2009, 102, 115–127. [Google Scholar] [CrossRef]
  46. Sone, I.; Olsen, R.L.; Sivertsen, A.H.; Eilertsen, G.; Heia, K. Classification of fresh Atlantic salmon (Salmo salarL.) fillets stored under different atmospheres by hyperspectral imaging. J. Food Eng. 2012, 109, 482–489. [Google Scholar] [CrossRef]
  47. Barbin, D.F.; ElMasry, G.; Sun, D.-W.; Allen, P. Nondestructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chem. 2013, 138, 1162–1171. [Google Scholar] [CrossRef] [PubMed]
  48. Kamruzzaman, M.; Makino, Y.; Oshita, S. Parsimonious model development for real-time monitoring of moisture in red meat using hyperspectral imaging. Food Chem. 2016, 196, 1084–1091. [Google Scholar] [CrossRef]
  49. Feng, L.; Zhang, M.; Adhikari, B.; Guo, Z. Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms. Food Anal. Methods 2019, 12, 914–925. [Google Scholar] [CrossRef]
  50. Cen, H.; Lu, R.; Ariana, D.P.; Mendoza, F. Hyperspectral imaging-based classification and waveband selection for internal defect detection of pickling cucumbers. Food Bioprocess Technol. 2014, 7, 1689–1700. [Google Scholar] [CrossRef]
  51. Cheng, J.-H.; Sun, D.-W. Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis. LWT Food Sci. Technol. 2015, 62, 1060–1068. [Google Scholar] [CrossRef]
  52. Cheng, J.-H.; Qu, J.-H.; Sun, D.-W.; Zeng, X.-A. Visible/near-infrared hyperspectral imaging prediction of textural firmness of grass carp (Ctenopharyngodon idella) as affected by frozen storage. Food Res. Int. 2014, 56, 190–198. [Google Scholar] [CrossRef]
  53. Gómez-Sanchís, J.; Lorente, D.; Soria-Olivas, E.; Aleixos, N.; Cubero, S.; Blasco, J. Development of a Hyperspectral Computer Vision System Based on Two Liquid Crystal Tuneable Filters for Fruit Inspection. Application to Detect Citrus Fruits Decay. Food Bioprocess Technol. 2014, 7, 1047–1056. [Google Scholar] [CrossRef]
  54. Su, W.-H.; Sun, D.-W. Potential of hyperspectral imaging for visual authentication of sliced organic potatoes from potato and sweet potato tubers and rapid grading of the tubers according to moisture proportion. Comput. Electron. Agric. 2016, 125, 113–124. [Google Scholar] [CrossRef]
  55. Gowen, A.; Odonnell, C.; Cullen, P.; Downey, G.; Frias, J. Hyperspectral imaging—An emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 2007, 18, 590–598. [Google Scholar] [CrossRef]
  56. Kamruzzaman, M.; ElMasry, G.; Sun, D.W.; Allen, P. Nondestructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chem. 2013, 141, 389–396. [Google Scholar] [CrossRef]
  57. Sun, D.-W.; Brosnan, T. Pizza quality evaluation using computer vision—Part 2—Pizza topping analysis. J. Food Eng. 2003, 57, 91–95. [Google Scholar] [CrossRef]
  58. ElMasry, G.; Barbin, D.F.; Sun, D.W.; Allen, P. Meat quality evaluation by hyperspectral imaging technique: An overview. Crit. Rev. Food Sci. Nutr. 2012, 52, 689–711. [Google Scholar] [CrossRef] [PubMed]
  59. Taghizadeh, M.; Gowen, A.A.; O’Donnell, C.P. Comparison of hyperspectral imaging with conventional RGB imaging for quality evaluation of Agaricus bisporus mushrooms. Biosyst. Eng. 2011, 108, 191–194. [Google Scholar] [CrossRef]
  60. Ravikanth, L.; Jayas, D.S.; White, N.D.G.; Fields, P.G.; Sun, D.W. Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products. Food Bioprocess Technol. 2017, 10, 1–33. [Google Scholar] [CrossRef]
  61. Xiong, Z.; Xie, A.; Sun, D.W.; Zeng, X.A.; Liu, D. Applications of hyperspectral imaging in chicken meat safety and quality detection and evaluation: A review. Crit. Rev. Food Sci. Nutr. 2015, 55, 1287–1301. [Google Scholar] [CrossRef] [PubMed]
  62. Su, W.-H.; Sun, D.-W.; He, J.-G.; Zhang, L.-B. Variation analysis in spectral indices of volatile chlorpyrifos and non-volatile imidacloprid in jujube (Ziziphus jujuba Mill.) using near-infrared hyperspectral imaging (NIR-HSI) and gas chromatography-mass spectrometry (GC–MS). Comput. Electron. Agric. 2017, 139, 41–55. [Google Scholar] [CrossRef]
  63. Tao, F.F.; Peng, Y.K. A nondestructive method for prediction of total viable count in pork meat by hyperspectral scattering imaging. Food Bioprocess Technol 2015, 8, 17–33. [Google Scholar] [CrossRef]
  64. Feng, Y.-Z.; Sun, D.-W. Application of hyperspectral imaging in food safety inspection and control: A review. Crit. Rev. Food Sci. Nutr. 2012, 52, 1039–1058. [Google Scholar] [CrossRef]
  65. Valous, N.A.; Mendoza, F.; Sun, D.-W.; Allen, P. Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. Meat Sci. 2009, 81, 132–141. [Google Scholar] [CrossRef]
  66. Mehl, P.M.; Chen, Y.R.; Kim, M.S.; Chan, D.E. Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J. Food Eng. 2004, 61, 67–81. [Google Scholar] [CrossRef]
  67. Kim, J.G.; Xia, M.; Liu, H. Extinction coefficients of hemoglobin for near-infrared spectroscopy of tissue. IEEE Eng. Med. Biol. Mag. 2005, 24, 118–121. [Google Scholar] [CrossRef] [PubMed]
  68. Nagata, M.; Tallada, J.; Kobayashi, T. Bruise detection using NIR hyperspectral imaging for strawberry (Fragaria ananassa Duch.). Environ. Control Biol. 2006, 44, 133–142. [Google Scholar] [CrossRef]
  69. Zhu, H.; Gowen, A.; Feng, H.; Yu, K.; Xu, J.L. Deep spectral-spatial features of near infrared hyperspectral images for pixel-wise classification of food products. Sensors 2020, 20, 5322. [Google Scholar] [CrossRef] [PubMed]
  70. Liang, H.F.; Geladi, P. Techniques and Applications of Hyperspectral Image Analysis; John Wiley & Sons Ltd.: West Sussex, UK, 2007; pp. 1–15. [Google Scholar]
  71. Li, X.L.; He, Y. Evaluation of least squares support vector machine regression and other multivariate calibrations in determination of internal attributes of tea beverages. Food Bioprocess Technol. 2010, 3, 651–661. [Google Scholar] [CrossRef]
  72. Lawrence, K.C.; Park, B.; Windham, W.R.; Mao., C. Calibration of A Pushbroom Hyperspectral Imaging System for Agricultural Inspection. Trans. ASAE 2003, 46, 513. [Google Scholar] [CrossRef]
  73. Burger, J.; Gowen, A. Data handling in hyperspectral image analysis. Chemom. Intell. Lab. Syst. 2011, 108, 13–22. [Google Scholar] [CrossRef]
  74. Su, W.-H.; Xue, H. Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality. Foods 2021, 10, 2146. [Google Scholar] [CrossRef]
  75. Amigo, J.M. Practical issues of hyperspectral imaging analysis of solid dosage forms. Anal. Bioanal. Chem.2010, 398, 93–109. [Google Scholar] [CrossRef]
  76. Amjad, W.; Hensel, O.; Munir, A.; Esper, A.; Sturm, B. Thermodynamic analysis of drying process in a diagonal-batch dryer developed for batch uniformity using potato slices. J. Food Eng. 2016, 169, 238–249. [Google Scholar] [CrossRef]
  77. Moscetti, R.; Haff, R.P.; Ferri, S.; Raponi, F.; Monarca, D.; Liang, P.; Massantini, R. Real-Time Monitoring of Organic Carrot (var. Romance) During Hot-Air Drying Using Near-Infrared Spectroscopy. Food Bioprocess Technol. 2017, 10, 2046–2059. [Google Scholar] [CrossRef]
  78. Su, W.-H.; Sun, D.-W. Comparative assessment of feature-wavelength eligibility for measurement of water binding capacity and specific gravity of tuber using diverse spectral indices stemmed from hyperspectral images. Comput. Electron. Agric. 2016, 130, 69–82. [Google Scholar] [CrossRef]
  79. Wang, S.; Tian, H.; Tian, S.; Yan, J.; Wang, Z.; Xu, H. Evaluation of dry matter content in intact potatoes using different optical sensing modes. J. Measure. Characterizat. 2022, 22, 1–6. [Google Scholar] [CrossRef]
  80. Pan, L.; Lu, R.; Zhu, Q.; McGrath, J.M.; Tu, K. Measurement of moisture, soluble solids, sucrose content and mechanical properties in sugar beet using portable visible and near-infrared spectroscopy. Postharvest Biol. Technol. 2015, 102, 42–50. [Google Scholar] [CrossRef]
  81. Tian, X.; Aheto, J.H.; Bai, J.; 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.2020, 45, e15128. [Google Scholar] [CrossRef]
  82. Su, W.H.; Sun, D.W. Rapid visualization of moisture migration in tuber during dehydration using hyperspectral imaging. In Proceedings of the CIGR-AgEng Conference, Aarhus, Denmark, 26–29 June 2016; pp. 26–29. [Google Scholar]
  83. Su, W.-H.; Sun, D.-W. Evaluation of spectral imaging for inspection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat (Triticum spp.) flour. J. Food Eng. 2017, 200, 59–69. [Google Scholar] [CrossRef]
  84. Khamsopha, D.; Teerachaichayut, S. Detection of Adulteration of Tapioca Starch with Dolomite by near Infrared Hyperspectral Imaging. Key Eng. Mater. 2020, 862, 46–50. [Google Scholar] [CrossRef]
  85. Meghar, K. SOP for Hyperspectral Imaging Analysis of Fresh RTB Crops. High-Throughput Phenotyping Protocols (HTPP), WP3; RTBfoods Project-CIRAD: Montpellier, France, 2020. [Google Scholar]
  86. Qiao, J.; Wang, N.; Ngadi, M.O. Water content and weight estimation for potatoes using hyperspectral imaging. In Proceedings of the 2005 ASAE Annual Meeting, Tampa, FL, USA, 17–20 July 2005; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2005; p. 1. [Google Scholar]
  87. Su, W.H.; Sun, D.W. Chemical imaging for measuring the time series variations of tuber dry matter and starch concentration. Comput. Electron. Agric. 2017, 140, 361–373. [Google Scholar] [CrossRef]
  88. Su, W.H.; Bakalis, S.; Sun, D.W. Chemometric determination of time series moisture in both potato and sweet potato tubers during hot air and microwave drying using near/mid-infrared (NIR/MIR) hyperspectral techniques. Dry. Technol. 2020, 38, 806–823. [Google Scholar] [CrossRef]
  89. Su, W.H.; Sun, D.W. Rapid determination of starch content of potato and sweet potato by using NIR hyperspectral imaging. Hortscience 2019, 54, S38. [Google Scholar]
  90. Wang, F.; Wang, C.; Song, S. A study of starch content detection and the visualization of fresh-cut potato based on hyperspectral imaging. RSC Adv. 2021, 11, 13636–13643. [Google Scholar] [CrossRef]
  91. Zhao, X.; Wang, W.; Chu, X.; Jiang, H.; Jia, B.; Yang, Y.; Kimuli, D.; Qin, H.; Dong, A. Rapid and nondestructive quantification of cassava starch adulterants in potato starch by using hyperspectral imaging. In Proceedings of the 2018 ASABE Annual International Meeting, St. Joseph, MI, USA, 15 February 2018; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2018; p. 1. [Google Scholar]
  92. Angel, D.-N.; Arno, F.; Pilar, C.; Esteban, V.-F.; Manuel, F.-D. Common Scab Detection on Potatoes Using an infrared hyperspectral imaging system. Image Anal. Process. 2011, 6979, 303–312. [Google Scholar]
  93. Angel, D.-N.; Arno, F.; Pilar, C.; Esteban, V.-F.; Manuel, F.-D. Non–destructive Detection of Hollow Heart in Potatoes Using Hyperspectral Imaging. Comput. Anal. Images Patterns 2011, 6855, 180–187. [Google Scholar]
  94. Evi Masithoh, R.; Amanah, H.Z.; Yoon, W.-S.; Joshi, R.; Cho, B.-K. Determination of protein and glucose of tuber and root flours using NIR and MIR spectroscopy. Infrared Phys. Technol. 2021, 113, 103577. [Google Scholar] [CrossRef]
  95. Heo, S.; Choi, J.-Y.; Kim, J.; Moon, K.-D. Prediction of moisture content in steamed and dried purple sweet potato using hyperspectral imaging analysis. Food Sci. Biotechnol. 2021, 30, 783–791. [Google Scholar] [CrossRef] [PubMed]
  96. Luo, S.; He, Y.; Li, Q.; Jiao, W.; Zhu, Y.; Zhao, X. Non-destructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage. Plant Methods2020, 16, 150. [Google Scholar] [CrossRef]
  97. Rady, A.M.; Guyer, D.E.; Kirk, W.; Donis-González, I.R. The potential use of visible/near infrared spectroscopy and hyperspectral imaging to predict processing-related constituents of potatoes. J. Food Eng. 2014, 135, 11–25. [Google Scholar] [CrossRef]
  98. Sun, W.; Feng, L.; Zhang, Z.; Ma, Y.; Crosby, T.; Naber, M.; Wang, Y. Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning. Sensors2020, 20, 5293. [Google Scholar] [CrossRef]
  99. Shao, Y.; Liu, Y.; Xuan, G.; Wang, Y.; Gao, Z.; Hu, Z.; Han, X.; Gao, C.; Wang, K. Application of hyperspectral imaging for spatial prediction of soluble solid content in sweet potato. RSC Adv. 2020, 10, 33148. [Google Scholar] [CrossRef]
  100. Somaratne, G.; Reis, M.M.; Ferrua, M.J.; Ye, A.; Nau, F.; Floury, J.; Dupont, D.; Singh, R.P.; Singh, J. Mapping the Spatiotemporal Distribution of Acid and Moisture in Food Structures during Gastric Juice Diffusion Using Hyperspectral Imaging. J. Agric. Food Chem. 2019, 67, 9399–9410. [Google Scholar] [CrossRef]
  101. Zhuang, H.; Ni, Y.; Kokot, S. A Comparison of Near- and Mid-Infrared Spectroscopic Methods for the Analysis of Several Nutritionally Important Chemical Substances in the Chinese Yam (Dioscorea opposita): Total Sugar, Polysaccharides, and Flavonoids. Appl. Spectrosc. 2015, 69, 488–495. [Google Scholar] [CrossRef]
  102. Do Trong, N.N.; Tsuta, M.; Nicolaï, B.M.; De Baerdemaeker, J.; Saeys, W. Prediction of optimal cooking time for boiled potatoes by hyperspectral imaging. J. Food Eng. 2011, 105, 617–624. [Google Scholar] [CrossRef]
  103. Ayvaz, H.; Rodriguez-Saona, L.E. Application of handheld and portable spectrometers for screening acrylamide content in commercial potato chips. Food Chem. 2015, 174, 154–162. [Google Scholar] [CrossRef] [PubMed]
  104. Ayvaz, H.; Bozdogan, A.; Giusti, M.M.; Mortas, M.; Gomez, R.; Rodriguez-Saona, L.E. Improving the screening of potato breeding lines for specific nutritional traits using portable mid-infrared spectroscopy and multivariate analysis. Food Chem. 2016, 211, 374–382. [Google Scholar] [CrossRef] [PubMed]
  105. López-Maestresalas, A.; Keresztes, J.C.; Goodarzi, M.; Arazuri, S.; Jarén, C.; Saeys, W. Nondestructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging. Food Control 2016, 70, 229–241. [Google Scholar] [CrossRef]
  106. Liu, Y.; Sun, Y.; Xie, A.; Yu, H.; Yin, Y.; Li, X.; Duan, X. Potential of Hyperspectral Imaging for Rapid Prediction of Anthocyanin Content of Purple-Fleshed Sweet Potato Slices During Drying Process. Food Anal. Methods 2017, 10, 3836–3846. [Google Scholar] [CrossRef]
  107. Moscetti, R.; Sturm, B.; Crichton, S.O.; Amjad, W. Massantini, R. Postharvest monitoring of organic potato (cv. Anuschka) during hot-air drying using visible-NIR hyperspectral imaging. J. Sci. Food Agric. 2017, 98, 2507–2517. [Google Scholar] [CrossRef]
  108. Teeken, B.; Agbona, A.; Bello, A.; Olaosebikan, O.; Alamu, E.; Adesokan, M.; Awoyale, W.; Madu, T.; Okoye, B.; Chijioke, U.; et al. Understanding cassava varietal preferences through pairwise ranking of gari-eba and fufu prepared by local farmer–processors. Int. J. Food Sci. Technol. 2021, 56, 1258–1277. [Google Scholar] [CrossRef] [PubMed]
  109. 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, 1, 94. [Google Scholar] [CrossRef]
  110. Pathare, P.B.; Opara, U.L.; Al-Said, F.A. Colour measurement and analysis in fresh and processed foods: A review. Food Bioprocess Technol. 2013, 6, 36–60. [Google Scholar] [CrossRef]
  111. Sanchez, P.D.; Hashim, N.; Shamsudin, R.; Nor, M.Z. Applications of imaging and spectroscopy techniques for non-destructive quality evaluation of potatoes and sweet potatoes: A review. Trends Food Sci. Technol. 2020, 96, 208–221. [Google Scholar] [CrossRef]
  112. Chen, L.; Opara, U.L. Texture measurement approaches in fresh and processed foods—A review. Food Res. Int. 2013, 51, 823–835. [Google Scholar] [CrossRef]

This entry is adapted from the peer-reviewed paper 10.3390/app13095226

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