Quality analysis, classification, and authentication of liquid foods by near-infrared spectroscopy: A review of recent research developments

References

• Alessandrini, L., Romani, S., Pinnavaia, G. and Rosa, M. D. (2008). Near infrared spectroscopy: An analytical tool to predict coffee roasting degree. Ann. Ahimica Acta. 625(1):95–102.
   PubMed Web of Science ®Google Scholar
• Andueza, D., Rouel, J., Chilliard, Y., Leroux, C. and Ferlay, A. (2013). Prediction of the goat milk fatty acids by near infrared reflectance spectroscopy.Eur. J. Lipid Sc. Technol. 115(6):612–620.
   Web of Science ®Google Scholar
• Armenta, S., Garrigues, S. and Guardia, M. D. L. (2007). Determination of edible oil parameters by near infrared spectrometry. Ann. Chim. Acta. 596:330–337.
   PubMed Web of Science ®Google Scholar
• Arnoult, V. M. R. and Soyeurt, H. (2009). Genetic variability of milk fatty acids. J. Appl. Genet. 50:29–39.
   PubMed Web of Science ®Google Scholar
• Azizian, H., Mossoba, M. M., Fardin-Kia, A. R., Delmonte, P., Karunathilaka, S. R. and Kramer, J. K. (2015). Novel, rapid identification, and quantification of adulterants in extra virgin olive oil using near-infrared spectroscopy and chemometrics. Lipids. 50(7):705–718.
   PubMed Web of Science ®Google Scholar
• Barbin, D., ElMasry, G., Sun, D.-W. and Allen, P. (2012). Near-infrared hyperspectral imaging for grading and classification of pork. Meat Science. 90(1):259–268.
   PubMed Web of Science ®Google Scholar
• Barbin, D. F., ElMasry, G., Sun, D.-W. and Allen, P. (2013). Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chemistry 118(2–3):1162–1171.
   Google Scholar
• Bendini, A., Cerretani, L., Di Virgilio, F., Belloni, P., Bonoli-Carbognin, M. and Lercker, G. (2007). Preliminary evaluation of the application of the FTIR spectroscopy to control the geographic origin and quality of virgin olive oils. J. Food Qual. 30:424–437.
   Web of Science ®Google Scholar
• Blanco, M., Peinado, A. C. and Mas, J. (2004). Analytical monitoring of alcoholic fermentation using NIR spectroscopy. Biotech. Bioeng. 88:536–542.
   PubMed Web of Science ®Google Scholar
• Caligiani, A., Acquotti, D., Palla, G. and Bocchi, V. (2007). Identification and quantification of the main organic components of vinegars by high resolution 1H NMR spectroscopy. Ann. Chim. Acta. 585:110–119.
   PubMed Web of Science ®Google Scholar
• Castineira, M. M., Feldmann, I., Jakubowski, N. and Andersson, J. T. (2004). Classification of German white wines with certified brand of origin by multielement quantification and pattern recognition techniques. J. Agric. Food Chem. 52:2962–2974.
   PubMed Web of Science ®Google Scholar
• Casale, M., Abajo, M. J. S., Sáiz, J. M. G., Pizarro, C. and Forina, M. (2006). Study of the aging and oxidation processes of vinegar samples from different origins during storage by near-infrared spectroscopy. AnN. Chim. Acta. 557:360–366.
   Web of Science ®Google Scholar
• Casale, M., Casolino, C., Ferrari, G. and Forina, M. (2008). Near infrared spectroscopy and class modeling techniques for the geographical authentication of ligurian extra virgin olive oil. J. Near Infrared Spectrosc. 16:39–47.
   Web of Science ®Google Scholar
• Cen, H., He, Y. and Huang, M. (2006). Measurement of soluble solids contents and pH in orange juice using chemometrics and Vis-NIRS. J. Agric. Food Chem. 54(20):7437–7443.
   PubMed Web of Science ®Google Scholar
• Cen, H., Bao, Y. and He, Y. (2006). Pattern recognition of visible and near-infrared spectroscopy from bayberry juices by use of partial least squares and a back propagation neural network. Appl. Optic. 45(29):7679–7683.
   PubMed Web of Science ®Google Scholar
• Cen, H., Bao, Y., He, Y. and Sun, D.-W. (2007). Visible and near infrared spectroscopy for rapid detection of citric and tartaric acids in orange juice. J. Food Eng.82(2):253–260.
   Web of Science ®Google Scholar
• Chen, J., Arnold, M. A. and Small, G. W. (2004). Comparison of combination and first overtone spectral regions of near-infrared calibration models for glucose and other biomolecules in aqueous solutions. Ann. Chem. 76(18):5405–5413.
   PubMed Web of Science ®Google Scholar
• Chen, H., Tan, C., Wu, T., Wang, L. and Zhu, W. (2014). Discrimination between authentic and adulterated liquors by near-infrared spectroscopy and ensemble classification.Spectrochim. Acta A. 130:245–249.
   PubMed Web of Science ®Google Scholar
• Chilliard, Y. and Ferlay, A. (2004). Dietary lipids and forages interactions on cow and goat milk fatty acid composition and sensory proprieties. Reprod. Nutr. Dev. 44:467–492.
   PubMedGoogle Scholar
• Chilliard, Y., Rouel, J., Ferlay, A., Bernard, L., Gaborit, P., Raynal-Ljutovac, K. and Buttriss, J. (2006). Optimising goat's milk and cheese fatty acid composition. Improving the Fat Content of Foods, pp. 281–312.
   Google Scholar
• Chilliard, Y., Rouel, J. and Leroux, C. (2006). Goat's alpha-s1 casein genotype influences its milk fatty acid composition and delta-9 desaturation ratios. Anim. Feed Sci. Technol.131(3):474–487.
   Web of Science ®Google Scholar
• Chilliard, Y., Glasser, F., Ferlay, A., Bernard, L., Rouel, J. and Doreau, M. (2007). Diet, rumen biohydrogenation and nutritional quality of cow and goat milk fat. Eur. J. Lipid Sci. Technol. 109(8):828–855.
   Web of Science ®Google Scholar
• Christy, A., Kasemsumran, S., Du, Y. P. and Ozaki, Y. (2004). The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics. Ann. Sci. 20:935–940.
   Google Scholar
• Christy, A. and Ozaki, Y. (2005). Partial least squares processing of near-infrared spectra for discrimination and quantification of adulterated olive oils. Spectrosc. Lett. 38:839–851.
   Web of Science ®Google Scholar
• Cipolat-Gotet, C., Cecchinato, A., De Marchi, M., Penasa, M. and Bittante, G. (2012). Comparison between mechanical and near-infrared methods for assessing coagulation properties of bovine milk.J. Dairy Sci. 95(11):6806–6819.
   PubMed Web of Science ®Google Scholar
• Cocchi, M., Durante, C., Foca, G., Manzini, D., Marchetti, A. and Ulrici, A. (2004). Application of a wavelet-based algorithm on HS-SPME/GC signals for the classification of balsamic vinegars. Chemometr. Intell. Lab. Syst. 71:129–140.
   Web of Science ®Google Scholar
• Coppa, M., Ferlay, A., Leroux, C., Jestin, M., Chilliard, Y., Martin, B. and Andueza, D. (2010). Prediction of milk fatty acid composition by near infrared reflectance spectroscopy. Int. Dairy J. 20(3):182–189.
   Web of Science ®Google Scholar
• Costa, R. S., Santos, S. R. B., Almeida, L. F., Nascimento, E. C. L., Pontes, M. J. C., Lima, R. A. C., Simõesa, S. S. and Araújo, M. C. U. (2004). A novel strategy to verification of adulteration in alcoholic beverages based on Schlieren effect measurements and chemometric techniques. Microchem. J. 78:27–34.
   Web of Science ®Google Scholar
• Cozzolino, D., Kwiatkowski, M. J., Parker, M., Cynkar, W. U., Dambergs, R. G., Gishen, M. and Herderich, M. J. (2004). Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Anal. Chim. Acta. 513:73–80.
   Web of Science ®Google Scholar
• Cozzolino, D., Parker, M., Dambergs, R. G., Herderich, M. and Gishen, M. (2006). Chemometrics and visible-near infrared spectroscopic monitoring of red wine fermentation in a pilot scale. Biotech. Bioeng. 95:1101–1107.
   PubMed Web of Science ®Google Scholar
• Cozzolino, D., Kwiatkowski, M. J., Dambergs, R. G., Cynkar, W. U., Janik, L. J., Skouroumounis, G. and Gishen, M. (2008a). Analysis of elements in wine using near infrared spectroscopy and partial least squares regression. Talanta. 74(4):711–716.
   PubMed Web of Science ®Google Scholar
• Cozzolino, D., Cowey, G., Lattey, K. A., Godden, P., Cynkar, W. U., Dambergs, R. G., Janik, L. and Gishen, M. (2008b). Relationship between wine scores and visible–near-infrared spectra of Australian red wines. Ann. Bioanal. Chem. 391(3):975–981.
   PubMed Web of Science ®Google Scholar
• Cozzolino, D., Cynkar, W., Shah, N. and Smith, P. (2012). Varietal differentiation of grape juice based on the analysis of near-and mid-infrared spectral data. Food Ann. Methods. 5(3):381–387.
   Web of Science ®Google Scholar
• Cozzolino, D. (2015). The role of visible and infrared spectroscopy combined with chemometrics to measure phenolic compounds in grape and wine samples. Molecules. 20(1):726–737.
   PubMed Web of Science ®Google Scholar
• Cui, Z.-W., Sun, L.-J., Chen, W. and Sun, D.-W. (2008). Preparation of dry honey by microwave-vacuum drying. J. Food Engin. 84(4):582–590.
   Web of Science ®Google Scholar
• Davrieux, F., Manez, J. C., Durand, N. and Guyot, B. (2004). Determination of the content of six major biochemical compounds of green coffee using NIR. In: Proceedings of 11th International Conference on Near Infrared Spectroscopy. Vol. 441.
   Google Scholar
• Diaz-Carrillo, E., Munoz-Serrano, A., Alonso-Moraga, A. and Serradilla-Manrique, J. M. (2004). Near infrared calibrations for goat's milk components: Protein, total casein, αs-, β- and κ-caseins, fat and lactose. J. Near Infrared Spectrosc. 1(3):141–146.
   Google Scholar
• Di Egidio, V., Sinelli, N., Giovanelli, G., Moles, A. and Casiraghi, E. (2010). NIR and MIR spectroscopy as rapid methods to monitor red wine fermentation. Eur. Food Res. Technol. 230(6):947–955.
   Web of Science ®Google Scholar
• Dračková, M., Hadra, L., Janštová, B., Navrátilová, P., Přidalová, H. and Vorlová, L. (2008). Analysis of goat milk by near-infrared spectroscopy. Acta Vet. Brno. 77(3):415–442.
   Web of Science ®Google Scholar
• Dugo, G., La Pera, L., Pellicanó, T. M., Di Bella, G. and D'Imperio, M. (2005). Determination of some inorganic anions and heavy metals in DOC Golden and Amber Marsala wines: Statistical study of the influence of ageing period, colour and sugar content. Food Chem. 91(2):355–363.
   Web of Science ®Google Scholar
• Eglinton, J., Griesser, M., Henschke, P., Kwiatkowski, M., Parker, M and Herderich, M. (2004). Yeast-mediated formation of pigmented polymers in red wine. In: Red Wine Color: Exploring the Mysteries, pp. 9–21. Waterhouse, A. L. and Kennedy, J. A. Eds., American Chemical Society, Washington, DC.
   Google Scholar
• ElMasry, G., Kamruzzaman, M., Sun, D.-W. and Allen, P. (2012). Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review. Critical Reviews in Food Science and Nutrition. 52:999–1023.
   PubMed Web of Science ®Google Scholar
• ElMasry, G., Barbin, D. F., Sun, D.-W. and Allen, P. (2012). Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview. Critical Reviews in Food Science and Nutrition 52(8):689–711.
   PubMed Web of Science ®Google Scholar
• Esteban-Diez, I., González-Sáiz, J. M. and Pizarro, C. (2004). Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy. Ann. Chim. Acta. 525(2):171–182.
   Web of Science ®Google Scholar
• Esteban-Diez, I., Gonzalez-Saiz, J. M., Sáenz-González, C. and Pizarro, C. (2007). Coffee varietal differentiation based on near infrared spectroscopy. Talanta. 71(1):221–229.
   PubMed Web of Science ®Google Scholar
• Fagan, C. C., Castillo, M., Payne, F. A., O'Donnell, C. P. and O'Callaghan, D. J. (2007). Effect of cutting time, temperature, and calcium on curd moisture, whey fat losses, and curd yield by response surface methodology. J. Dairy Sci. 90:4499–4512.
   PubMed Web of Science ®Google Scholar
• Fang, Z., Zhang, M. and Wang, L. (2007). HPLC-DAD-ESIMS analysis of phenolic compounds in bayberries (Myricarubra Sieb. Et Zucc.). Food Chem. 100(2):845–852.
   Web of Science ®Google Scholar
• Farah, A., Monteiro, M. C., Calado, V., Franca, A. S. and Trugo, L. C. (2006). Correlation between cup quality and chemical attributes of Brazilian coffee. Food Chem. 98(2):373–380.
   Web of Science ®Google Scholar
• Ferlay, A., Martin, B., Pradel, P., Coulon, J. B. and Chilliard, Y. (2006). Influence of grass-based diets on milk fatty acid composition and milk lipolytic system in tarentaise and montbe′ liarde cow breeds. J. Dairy Sci. 89:4026–4041.
   PubMed Web of Science ®Google Scholar
• Feng, Y.-Z. and Sun, D.-W. (2012). Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review. Critical Reviews in Food Science and Nutrition. 52:1039–1058.
   PubMed Web of Science ®Google Scholar
• Feng, Y.-Z. and Sun, D.-W. (2013). Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. Talanta 109:74–83.
   PubMed Web of Science ®Google Scholar
• Feng. Y.-Z., ElMasry, G., Sun, D.-W., Scannell, A. G. M., Walsh, D. and Morcy, N. (2013b). Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets. Food Chemistry. 138(2–3): 1829–1836.
   Web of Science ®Google Scholar
• Francis, L., Hoi, P., Dambergs, R. G., Gishen, M., de Barros Lopes, M., Godden, P. and Pretorius, I. (2005). Objective measures of grape quality–are they achievable? Australian and New Zealand Wine Industry Journal. 20:12–18.
   Google Scholar
• Finn, B., Harvey, L. M. and McNeil, B. (2006). Near-infrared spectroscopic monitoring of biomass, glucose, ethanol and protein content in a high cell density Baker's yeast fed-batch bioprocess. Yeast. 23:507–517.
   PubMed Web of Science ®Google Scholar
• Fox, G. and Cruickshank, A. (2005). Near infrared reflectance as a rapid and inexpensive surrogate measure for fatty acid composition and oil content of peanuts (Arachishypogaea L.). J. Infrared Spectrosc. 13:287–291.
   Web of Science ®Google Scholar
• Francis, I. L., Høj, P. B., Dambergs, R. G., Gishen, M., de Barros Lopes, M. A., Pretorius, I. S., Godden, P. W., Henschke, P. A., Herderich, M. J. and Waters, E. J. (2005). Objective measures of grape quality – Are they achievable? In: Proceedings 12th Australian Wine Industry Technical Conference, Melbourne, Australia, pp. 85–90. Blair, R. J., Williams, P. J. and Pretorius, I. S., Eds.
   Google Scholar
• Franca, A. S., Mendonça, J. C. and Oliveira, S. D. (2005). Composition of green and roasted coffees of different cup qualities. LWT Food Sci. Technol. 38(7):709–715.
   Web of Science ®Google Scholar
• Fu, X. G., Yan, G. Z., Chen, B. and Li, H. B. (2005). Application of wavelet transforms to improve prediction precision of near infrared spectra. J. Food Eng. 69:461–466.
   Web of Science ®Google Scholar
• Galtier, O., Dupuya, N., Le Dr′eau, Y., Ollivier, D., Pinatel, C., Kister, J. and Artaud, J. (2007). Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra. Ann. Chim. Acta. 595:136–144.
   PubMed Web of Science ®Google Scholar
• Galtier, O., Le, D. Y., Olliver, D., Kister, J., Artaud, J. and Dupuy, N. (2008). Lipid compositions and French registered designations of origins of virgin olive oils predicted by chemometric analysis of mid-infrared spectra. Appl. Spectrosc. 62:583–590.
   PubMed Web of Science ®Google Scholar
• Gestal, M., Gómez-Carracedo, M. P., Andrade, J. M., Dorado, J., Fernández, E., Prada, D. and Pazos, A. (2004). Classification of apple beverages using artificial neural networks with previous variable selection. Ann. Chim. Acta. 524(1–2):225–234.
   Web of Science ®Google Scholar
• Ghasemi-Varnamkhasti, M. and Forina, M. (2014). NIR spectroscopy coupled with multivariate computational tools for qualitative characterization of the aging of beer. Comput. Electron. Agr. 100:34–40.
   Web of Science ®Google Scholar
• Giannoccaro, E., Wang, Y. J. and Chen, P. (2008). Comparison of two HPLC systems and an enzymatic method for quantification of soybean sugars. Food Chem. 106(1):324–330.
   Web of Science ®Google Scholar
• Guimet, F., Ferré, J., Boqué, R. and Rius, F. X. (2004). Application of unfold principal component analysis and parallel factor analysis to the exploratory analysis of olive oils by means of excitation–emission matrix fluorescence spectroscopy. Ann. Chim. Acta. 515(1):75–85.
   Web of Science ®Google Scholar
• Guimet, F., Boqué, R. and Ferré, J. (2004). Cluster analysis applied to the exploratory analysis of commercial Spanish olive oils by means of excitation-emission fluorescence spectroscopy. J. Agr. Food Chem. 52(22):6673–6679.
   PubMed Web of Science ®Google Scholar
• He, Y., Wu, D., Feng, S. and Li, X. (2007). Fast measurement of sugar content of yogurt using Vis/NIR spectroscopy. Int. J. Food Prop. 10(1):1–7.
   Web of Science ®Google Scholar
• Herderich, M. and Smith, P. A. (2005). Analysis of grape and wine tannins: Methods, applications and challenges. Aust. J. Grape Wine Res. 11:205–215.
   Web of Science ®Google Scholar
• Hsieh, C. L., Hung, C. Y. and Lin, M. J. (2011). Application of radial bases function network and response surface method to quantify compositions of raw goat milk with visible/near infrared spectroscopy. In Engineering Applications of Neural Networks (pp. 156–161). Springer Berlin Heidelberg.
   Google Scholar
• Hu, Y. X., Yuan, S. X., Li, J. N., Zhang, P. and Zhu, Q. M. (2005). Detection for the adulteration of citrus juice by fluorescence spectra. Sci. Technol. Food Ind. 26:166–168.
   Google Scholar
• Huang, H., Yu, H., Xu, H. and Ying, Y. (2008). Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: A review. J. Food Eng. 87(3):303–313.
   Web of Science ®Google Scholar
• Innawong, B., Mallikarjunan, P., Irundayaraj, J. and Marcy, J. E. (2004). The determination of frying oil quality using Fourier transform infrared attenuated total reflectance. Lebensm Wiss. U. Technol. 37:23–28.
   Web of Science ®Google Scholar
• Jackman, P., Sun, D.-W. and Allen, P. (2009). Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. Meat Science 83(2): 187–194.
   PubMed Web of Science ®Google Scholar
• Kamruzzaman, M., ElMasry, G., Sun, D.-W. and Allen. P. (2013). Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chemistry 141(1):389–396.
   PubMed Web of Science ®Google Scholar
• Kamruzzaman, M., ElMasry, G., Sun, D.-W. and Allen, P. (2012). Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovative Food Science and Emergeing Technologies. 16:218–226.
   Web of Science ®Google Scholar
• Kawasaki, M., Kawamura, S., Nakatsuji, H., and Natsuga, M. (2005). Online real-time monitoring of milk quality during milking by near-infrared spectroscopy. In 2005 ASAE Annual Meeting (p. 1). American Society of Agricultural and Biological Engineers.
   Google Scholar
• Kawasaki, M., Kawamura, S., Tsukahara, M., Morita, S., Komiya, M. and Natsuga, M. (2008). Near-infrared spectroscopic sensing system for on-line milk quality assessment in a milking robot.Comp. Electron. Agr. 63(1):22–27.
   Web of Science ®Google Scholar
• Kiani, H., Zhang, Z., Delgado, A. and Sun, Da-Wen. (2011). Ultrasound assisted nucleation of some liquid and solid model foods during freezing. Food Res. International 44(9): 2915–2921.
   Web of Science ®Google Scholar
• Kubarsepp, I., Henno, M., Kart, O. and Tupasela, T. (2005). A comparison of the methods for determination of the rennet coagulation properties of milk. Acta Agric. Scand. A. 55:145–148.
   Web of Science ®Google Scholar
• Lagardere, L., Lechat, H. and Lacoste, F. (2004). Quantitative determination of peroxide value (PV) and acidity in virgin olive oils and in sunflower oils thanks to the rapid Fourier transform near-infrared (FT-NIR) spectroscopic method. OCL Montrouge. 11:70–75.
   Google Scholar
• Li, Y. M. and Miao, L. F. (2004). The identification methods in laboratory for adulterant in milk. Diary Guide. 12:30–31.
   Google Scholar
• Li, X. and He, Y. (2006). Non-destructive measurement of acidity of Chinese bayberry using Vis/NIRS techniques. Eur. Food Res. Technol. 223(6):731–736.
   Web of Science ®Google Scholar
• Li, X., He, Y. and Fang, H. (2007). Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy. J. Food Eng. 81(2):357–363.
   Web of Science ®Google Scholar
• Liu, L., Cozzolino, D., Cynkar, W. U., Gishen, M. and Colby, C. B. (2006). Geographic classification of Spanish and Australian Tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. J. Agr. Food Chem. 54(18):6754–6759.
   PubMed Web of Science ®Google Scholar
• Liu, D., Sun, D.-W. and Zeng, X.-A. (2014). Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry. Food and bioprocess Technology 7(2):307–323.
   Web of Science ®Google Scholar
• Liu, F., and He, Y. (2008, February). Prediction of pH of cola beverage using Vis/NIR spectroscopy and least squares-support vector machine. In International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007 (pp. 66211Z–66211Z). International Society for Optics and Photonics.
   Google Scholar
• Liu, F., He, Y., Wang, L. and Pan, H. (2007). Feasibility of the use of visible and near infrared spectroscopy to assess soluble solids content and pH of rice wines. J. Food Eng. 83(3):430–435.
   Web of Science ®Google Scholar
• Liu, F. and He, Y. (2007). Use of visible and near infrared spectroscopy and least squares-support vector machine to determine soluble solids content and pH of cola beverage. J. Agr. Food Chem. 55(22):8883–8888.
   PubMed Web of Science ®Google Scholar
• Liu, L., Cozzolino, D., Cynkar, W. U., Dambergs, R. G., Janik, L., O'Neill, B. K., Colby, C. B. and Gishen, M. (2008a). Preliminary study on the application of visible-near infrared spectroscopy and chemometrics to classify Riesling wines from different countries. Food Chem. 106(2):781–786.
   Web of Science ®Google Scholar
• Liu, F., He, Y. and Wang, L. (2008b). Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis. Ann. Chim. Acta. 615:10–17.
   PubMed Web of Science ®Google Scholar
• Liu, F., Jiang, Y. and He, Y. (2009). Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer. Ann. Chim. Acta. 635(1):45–52.
   PubMed Web of Science ®Google Scholar
• Liu, F. and He, Y. (2009). Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar. Food Chem. 115:1430–1436.
   Web of Science ®Google Scholar
• Liu, F., He, Y., Wang, L. and Sun, G. (2011). Detection of organic acids and pH of fruit vinegars using near-infrared spectroscopy and multivariate calibration. Food Bioprocess. Technol. 4(8):1331–1340.
   Web of Science ®Google Scholar
• Liu, D., Zeng, X. A. and Sun, D.-W. (2013). NIR spectroscopy and imaging techniques for evaluation of fish quality – A review. Appl. Spectrosc. Rev. 48(8):609–628.
   Web of Science ®Google Scholar
• Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., and Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology. 5(4):1121–1142.
   Web of Science ®Google Scholar
• Ma, J., Zhang, H., Tuchiya, T., Miao, Y. and Chen, J. Y. (2014). Rapid determination of degradation of frying oil using near-infrared spectroscopy. Food Sci. Technol. Res. 20(2):217–223.
   Web of Science ®Google Scholar
• Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J., Saeys, W., and Nicolaï, B. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology. 5(2):425–444.
   Web of Science ®Google Scholar
• Mailer, R. J. (2004). Rapid evaluation of olive oil quality by NIR reflectance spectroscopy. J. Am. Oil Chem. Soc. 81(9):823–827.
   Web of Science ®Google Scholar
• Marquez, A. J., Díaz, A. M. and Reguera, M. I. (2005). Using optical NIR sensor for on-line virgin olive oils characterization. Sensor Actuators B. 107(1):64–68.
   Web of Science ®Google Scholar
• Martelo-Vidal, M. J. and Vázquez, M. (2014). Determination of polyphenolic compounds of red wines by UV–VIS–NIR spectroscopy and chemometrics tools. Food Chem. 158:28–34.
   PubMed Web of Science ®Google Scholar
• Mateo, M. J., O'Callaghan, D. J., Everard, C. D., Castillo, M., Payne, F. A. and O'Donnell, C. P. (2009). Validation of a curd-syneresis sensor over a range of milk composition and process parameters. J. Dairy Sci. 92:5386–5395.
   PubMed Web of Science ®Google Scholar
• Mba, O., Adewale, P., Dumont, M. J. and Ngadi, M. (2014). Application of near-infrared spectroscopy to characterize binary blends of palm and canola oils. Ind. Crop. Prod. 61:472–478.
   Web of Science ®Google Scholar
• McDonald, K. and Sun, D.-. (2001). Effect of evacuation rate on the vacuum cooling process of a cooked beef product. Journal of Food Engineering 48(3):195–202.
   Web of Science ®Google Scholar
• Meng, Q., Murray, S. C., Mahan, A., Collison, A., Yang, L. and Awika, J. (2015). Rapid estimation of phenolic content in colored maize by near-infrared reflectance spectroscopy and its use in breeding. Crop Sci. 55(5):2234–2243.
   Web of Science ®Google Scholar
• Moreira, J. L. and Santos, L. (2005). Analysis of organic acids in wine by Fourier-transform infrared spectroscopy. Anal. Bioanal. Chem. 382:421–425.
   PubMed Web of Science ®Google Scholar
• Morata, A., Go´mez-Cordove´s, M. C., Caldero´n, F. and Sua´rez, J. A. (2006). Effects of pH, temperature and SO2 on the formation of pyranoanthocyanins during red wine fermentation with two species of Saccharomyces. Int. J. Food Microbiol. 106(2):123–129.
   PubMed Web of Science ®Google Scholar
• Morgano, M. A., Faria, C. G., Ferrão, M. F. and Ferreira, M. M. (2007). Determinação de açúcar total em café cru porespectroscopia no infravermelhopróximo e regressãopormínimosquadradosparciais. Química Nova. 30(2):346–350.
   Web of Science ®Google Scholar
• Muik, B., Lendl, B., Molina-Díaz, A. and Pérez-Villarejo, L. (2004). Determination of oil and water content in olive pomace using near infrared and Raman spectrometry. A comparative study. Ann. Bioanal. Chem. 379(1):35–41.
   PubMed Web of Science ®Google Scholar
• Navra´til, M., Cimander, C. and Mandenius, C. F. (2004). On-line multisensory monitoring of yogurt and filmjo¨ lk fermentations on production scale. J. Agr. Food Chem. 52:415–420.
   PubMed Web of Science ®Google Scholar
• Ng, C. L., Wehling, R. L. and Cuppett, S. L. (2007). Method for determining frying oil degradation by near-infrared spectroscopy. J. Agr. Food Chem. 55(3):593–597.
   PubMed Web of Science ®Google Scholar
• Núñez-Sánchez, N., Martínez-Marín, A. L., Polvillo, O., Fernández-Cabanás, V. M., Carrizosa, J., Urrutia, B. and Serradilla, J. M. (2016). Near Infrared Spectroscopy (NIRS) for the determination of the milk fat fatty acid profile of goats. Food Chemistry. 190:244–252.
   PubMed Web of Science ®Google Scholar
• Oliveira, S. R., Oliveira, A. P. and Neto, J. A. G. (2007). Tungsten permanent chemical modifier with co-injection of Pd(NO3)2 + Mg(NO3)2 for direct determination of Pb in vinegar by graphite furnace atomic absorption spectrometry. Food Chem. 105:236–241.
   Web of Science ®Google Scholar
• Ozdemir, D. and Ozturk, B. (2007). Near infrared spectroscopic determination of olive oil adulteration with sunflower and corn oil. J. Food Drug Ann. 15:40–47.
   Web of Science ®Google Scholar
• Pereira, A. F. C., Pontes, M. J. C., Neto, F. F. G., Santos, S. R. B., Galvão, R. K. H. and Araújo, M. C. U. (2008). NIR spectrometric determination of quality parameters in vegetable oils using <i> iPLS and variable selection. Food Res. Int. 41(4):341–348.
   Web of Science ®Google Scholar
• Petisco, C., García-Criado, B., de Aldana, B. V., Zabalgogeazcoa, I. and Mediavilla, S. (2005). Use of near-infrared reflectance spectroscopy in predicting nitrogen, phosphorus and calcium contents in heterogeneous woody plant species. Ann. Bioanal. Chem. 382(2):458–465.
   PubMed Web of Science ®Google Scholar
• Pontes, M. J. C., Santos, S. R. B., Araujo, M. C. U., Almeida, L. F., Lima, R. A. C., Gaiao, E. N. and Souto, U. T. C. P. (2006). Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry. Food Res. Int. 39(2):182–189.
   Web of Science ®Google Scholar
• Qiao, T., Ren, J., Craigie, C., Zabalza, J., Maltin, C. and Marshall, S. (2015). Quantitative prediction of beef quality using visible and NIR spectroscopy with large data samples under industry conditions. J. Appl. Spectrosc. 82(1):137–144.
   Web of Science ®Google Scholar
• Rao, Y., Xiang, B., Zhou, X., Wang, Z., Xie, S. and Xu, J. (2009). Quantitative and qualitative determination of acid value of peanut oil using near-infrared spectrometry. J. Food Eng. 93(2):249–252.
   Web of Science ®Google Scholar
• Reid, L. M., O'Donnell, C. P. and Downey, G. (2006). Recent technological advances for the determination of food authenticity. Trends Food Sci. Technol. 17(7):344–353.
   Web of Science ®Google Scholar
• Ribeiro, J. S., Ferreira, M. M. C. and Salva, T. J. G. (2011). Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy. Talanta. 83(5):1352–1358.
   PubMed Web of Science ®Google Scholar
• Saiz-Abajo, M. J., Gonzalez-Saiz, J. M. and Pizarro, C. (2004). Classification of wine and alcohol vinegar samples based on near-infrared spectroscopy. Feasibility study on the detection of adulterated vinegar samples. J. Agr. Food Chem. 52(25):7711–7719.
   PubMed Web of Science ®Google Scholar
• Sáiz-Abajo, M. J., Gonzákez-Sáiz, J. M. and Pizarro, C. (2006). Prediction of organic acids and other quality parameters of wine vinegar by near-infrared spectroscopy. A feasibility study. Food Chem. 99:615–621.
   Web of Science ®Google Scholar
• Saurina, J. (2010). Characterization of wines using compositional profiles and chemometrics. Trac. Trends Ann. Chem. doi:10.1016/j.trac.2009.11.008
   Google Scholar
• Scordino, M., Di Mauro, A., Passerini, A. and Maccarone, E. (2004). Phloroglucinol from phlorin hydrolysis for testing quality of commercial orange juices and beverages. J. Agr. Food Chem. 52(25):7469–7474.
   PubMed Web of Science ®Google Scholar
• Shao, Y., He, Y. and Feng, S. (2007). Measurement of yogurt internal quality through using Vis/NIR spectroscopy. Food Res. Int. 40(7):835–841.
   Web of Science ®Google Scholar
• Shao, Y. N. and He, Y. (2007). Nondestructive measurement of the internal quality of bayberry juice using Vis/NIR spectroscopy. J. Food Eng. 79(3):1015–1019.
   Web of Science ®Google Scholar
• Shen, Y., Chen, S., Du, R., Xiao, Z., Huang, Y., Rasco, B. A. and Lai, K. (2014). Rapid assessment of the quality of deep frying oils used by street vendors with Fourier transform infrared spectroscopy. J. Food Meas.Charact. 8(4):336–342.
   Web of Science ®Google Scholar
• Shen, F., Yang, D., Ying, Y., Li, B., Zheng, Y. and Jiang, T. (2012). Discrimination between Shaoxing wines and other Chinese rice wines by near-infrared spectroscopy and chemometrics. Food Bioprocess Technol. 5(2):786–795.
   Web of Science ®Google Scholar
• Sinelli, N., Casiraghi, E., Tura, D. and Downey, G. (2008). Characterisation and classification of Italian virgin olive oils by near- and mid-infrared spectroscopy. J. Near Infrared Spectrosc. 16:335–342.
   Web of Science ®Google Scholar
• Sinelli, N., Cerretani, L., Egidio, V. D., Bendini, A. and Casiraghi, E. (2010). Application of near (NIR) infrared and mid (MIR) infrared spectroscopy as a rapid tool to classify extra virgin olive oil on the basis of fruity attribute intensity. Food Res. Int. 43(1):369–375.
   Web of Science ®Google Scholar
• Smyth, H. E., Cozzolino, D., Cynkar, W. U., Dambergs, R. G., Sefton, M. and Gishen, M. (2008). Near infrared spectroscopy as a rapid tool to measure volatile aroma compounds in Riesling wine: Possibilities and limits. Ann. Bioanal. Chem. 390(7):1911–1916.
   PubMed Web of Science ®Google Scholar
• Sodini, I., Remeuf, F., Haddad, S. and Corrieu, G. (2004). The relative effect of milk base, starter, and process on yogurt texture: a review. Critical Reviews in Food Science and Nutrition. 44(2):113–137.
   PubMed Web of Science ®Google Scholar
• Soyeurt, H., Dardenne, P., Dehareng, F., Lognay, G., Veselko, D., Marlier, M., Bertozzi, C., Mayeres, P. and Gengler, N. (2006). Estimating fatty acid content in cow milk using mid-infrared spectrometry. J. Dairy Sci. 89(9):3690–3695.
   PubMed Web of Science ®Google Scholar
• Sun, D.-W. and Woods, J. L. (1994). Low-temperature moisture transfer characteristics of wheat in thin-layers. Transactions of the ASAE 27(6):1919–1926.
   Google Scholar
• Sun, D.-W. (1997). Solar powered combined ejector vapour compression cycle for air conditioning and refrigeration. Energy Conversion and Management. 38(5):479–491.
   Web of Science ®Google Scholar
• Sun, G. C., Wu, B. G. and Liao, X. X. (2006). Study on improving rice wine acidity. China Brewing. 4:52–53.
   Google Scholar
• Sundaram, J., Kandala, C. V., Holser, R. A., Butts, C. L. and Windham, W. R. (2010). Determination of in-shell peanut oil and fatty acid composition using near-infrared reflectance spectroscopy. J. Am. Oil Chem. Soc. 87(10):1103–1114.
   Web of Science ®Google Scholar
• Szlyk, E., Szydlwska-Czerniak, A. and Kowalczyk-Marzec, A. (2005). NIR spectroscopy and partial least squares regression for determination of natural alpha-tocopherol in vegetable oils. J. Agr. Food Chem. 53:6980–6987.
   PubMed Web of Science ®Google Scholar
• Tillman, B. L., Gorbet, D. W. and Person, G. (2006). Predicting oleic and linoleic acid concentration of single peanut seeds using near infrared reflectance spectroscopy. Crop Sci. 46:2121–2126.
   Web of Science ®Google Scholar
• Tsenkova, R., Atanassova, S., Morita, H., Ikuta, K., Toyoda, K., Iordanova, I. K. and Hakogi, E. (2006). Near infrared spectra of cow's milk for milk quality evaluation: Disease diagnosis and pathogen identification. J. Near Infrared Spectrosc. 14(6):363–370.
   Web of Science ®Google Scholar
• Uncu, O. and Ozen, B. (2015). Prediction of various chemical parameters of olive oils with Fourier transform infrared spectroscopy. LWT Food Sci. Tech.
   Web of Science ®Google Scholar
• Urbano-Cuadrado, M., de Castro, M. D. L., Perez-Juan, P. M., Garcia-Olmo, J. and Gomez-Nieto, M. A. (2004). Near infrared reflectance, spectroscopy and multivariate analysis in enology-determination or screening of fifteen parameters in different types of wines. Ann. Chim. Acta. 527(1):81–88.
   Web of Science ®Google Scholar
• Urbano-Cuadrado, M., Luque de Castro, M. D., Perez Juan, P. M. and Gómez-Nieto, M. A. (2005). Comparison and joint use of near infrared spectroscopy and Fourier transform mid-infrared spectroscopy for the determination of wine parameters. Talanta. 66(1):218–224.
   PubMed Web of Science ®Google Scholar
• Urtubia, A., Pe´rez-Correa, J. R., Meurens, M. and Agosin, E. (2004). Monitoring large-scale wine fermentations with infrared spectroscopy. Talanta. 64:778–784.
   PubMed Web of Science ®Google Scholar
• Urtubia, A., Pe´rez-Correa, J. R., Pizarro, F. and Agosin, E. (2008). Exploring the applicability of MIR spectroscopy to detect early indications of wine fermentation problems. Food Cont. 19:382–388.
   Web of Science ®Google Scholar
• Van Leeuwen, K. A., Prenzler, P. D., Ryan, D. and Camin, F. (2014). Gas chromatography‐combustion‐isotope ratio mass spectrometry for traceability and authenticity in foods and beverages. Compr. Rev. Food Sci. Food Safety. 13(5):814–837.
   Web of Science ®Google Scholar
• Wang, H. H. and Sun, D.-W. (2002). Melting characteristics of cheese: analysis of effect of cheese dimensions using computer vision techniques. Journal of Food Engineering 52(3):279–284.
   Web of Science ®Google Scholar
• Wang, L. J. and Sun, D-W. (2004). Effect of operating conditions of a vacuum cooler on cooling performance for large cooked meat joints. J. Food Engin 61(2):231–240.
   Web of Science ®Google Scholar
• Wang, J. G. and Xu, L. (2005). Features of millet wine and its prospects. Jiangsu Condiment Subsidiary Food. 22(6):5–9.
   Google Scholar
• Wang, L., Lee, F. S., Wang, X. and He, Y. (2006). Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chem. 95(3):529–536.
   Web of Science ®Google Scholar
• Wang, W. and Paliwal, J. (2007). Near-infrared spectroscopy and imaging in food quality and safety. Sens. Instrum. Food Qual. 1(4):193–207.
   Google Scholar
• Wang, L., Li, Z. F., He, Y. and Liu, F. (2008). Fast detection of sugar content in fruit vinegar using NIR spectroscopy. Spectrosc. Spect. Ann. 28(8):1810–1813.
   Web of Science ®Google Scholar
• Wang, Y., Yu, X., Chen, X., Yang, Y. and Zhang, J. (2014). Application of Fourier transform near-infrared spectroscopy to the quantification and monitoring of carbonyl value in frying oils. Ann. Meth. 6(19):7628–7633.
   Web of Science ®Google Scholar
• Weeranantanaphan, J., Downey, G., Allen, P. and Sun, D.-W. (2011). A review of near infrared spectroscopy in muscle food analysis: 2005–2010. J. Near Infrared Spectrosc. 19:61–104.
   Web of Science ®Google Scholar
• Woodcock, T., Downey, G. and O'Donnell, C. P. (2008). Better quality food and beverages: The role of near infrared spectroscopy. J. Near Infrared Spectrosc. 16:1–29.
   Web of Science ®Google Scholar
• Wu, D., He, Y., Nie, P., Cao, F. and Bao, Y. (2010). Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Ann. Chim. Acta. 659(1):229–237.
   PubMed Web of Science ®Google Scholar
• Wu, D. and Sun, D.-W. (2013a). Colour measurements by computer vision for food quality control - A review. Trends in Food Science & Technology. 29(1): 5–20.
   Web of Science ®Google Scholar
• Wu, D. and Sun, D.-W. (2013b). Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta 111:39–46.
   PubMed Web of Science ®Google Scholar
• Wu, D. and Sun, D.-W. (2013c). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review - Part I: Fundamentals. Innovative Food Science & Emerging Technologies 19:1–14.
   Web of Science ®Google Scholar
• Wu, Z., Li, H., Long, J., Xu, E., Xu, X., Jin, Z. and Jiao, A. (2015). Discrimination of Chinese rice wines of different geographical origins by UV–Vis spectroscopy and chemometrics. J. Inst. Brew. 121(1):167–174.
   Web of Science ®Google Scholar
• Xie, L., Ye, X., Liu, D. and Ying, Y. (2009). Quantification of glucose, fructose and sucrose in bayberry juice by NIR and PLS. Food Chem. 114(3):1135–1140.
   Web of Science ®Google Scholar
• Yavari, A. A., Hamedi, M. and Haghbin, S. (2009). Investigation of frying oil quality using VIS/NIR hyperspectral analysis. J. Am. Oil Chem. Soc. 86(10):941–947.
   Web of Science ®Google Scholar
• Ye, M., Yue, T., Yuan, Y. and Li, Z. (2014). Application of FT-NIR spectroscopy to apple wine for rapid simultaneous determination of soluble solids content, pH, total acidity, and total ester content. Food Bioprocess Technol. 7(10):3055–3062.
   Web of Science ®Google Scholar
• Yu, H. Y., Ying, Y. B., Fu, X. P. and Lu, H. S. (2006a). Quality determination of Chinese rice wine based on Fourier transform near infrared spectroscopy. J. Near Infrared Spectrosc. 14(1):37–44.
   Web of Science ®Google Scholar
• Yu, H., Ying, Y., Fu, X. and Lu, H. (2006b). Classification of Chinese rice wine with different marked ages based on near infrared spectroscopy. J. Food Qual. 29(4):339–352.
   Web of Science ®Google Scholar
• Yu, H. Y., Zhou, Y., Fu, X. P., Xie, L. J. and Ying, Y. B. (2007a). Discrimination between Chinese rice wines of different geographical origins by NIRS and AAS. Eur. Food Res. Technol. 225(3–4):313–320.
   Web of Science ®Google Scholar
• Yu, H. Y., Ying, Y. B., Sun, T., Niu, X. Y. and Pan, X. X. (2007b). Vintage year determination of bottled rice wine by Vis–NIR spectroscopy. J. Food Sci. 72:125–129.
   Web of Science ®Google Scholar
• Yu, H., Lin, H., Xu, H., Ying, Y., Li, B. and Pan, X. (2008). Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy. J. Agr. Food Chem. 56(2):307–313.
   PubMed Web of Science ®Google Scholar
• Zhang, L. G., Zhang, X., Ni, L. J., Xue, Z. B., Gu, X. and Huang, S. X. (2014). Rapid identification of adulterated cow milk by non-linear pattern recognition methods based on near infrared spectroscopy. Food Chem. 145:342–348.
   PubMed Web of Science ®Google Scholar
• Zeaiter, M., Roger, J. M. and Bellon-Maurel, V. (2006). Dynamic orthogonal projection. A new method to maintain the on-line robustness of multivariate calibrations. Application to NIR-based monitoring of wine fermentations. Chemometr. Intell. Lab. Syst. 80:227–235.
   Web of Science ®Google Scholar
• Zhang, G., Ni, Y., Churchill, J. and Kokot, S. (2006). Authentication of vegetable oils on the basis of their physico-chemical properties with the aid of chemometrics. Talanta. 70:293–300.
   PubMed Web of Science ®Google Scholar
• Zheng, L. Y. and Sun, D-W. (2004). Vacuum cooling for the food industry - a review of recent research advances. Trends in Food Sci. and Tech. 15(12):555–568.
   Web of Science ®Google Scholar
• Zhu, F., Zhang, D., He, Y., Liu, F. and Sun, D.-W. (2013). Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen–thawed fish fillets. Food and Bioprocess Technology. 6(10):2931–2937.
   Web of Science ®Google Scholar
• Zhou, Y., Liu, T., Li, J. and Chen, Z. (2015). Rapid identification of edible oil and swill-cooked dirty oil by using near-infrared spectroscopy and sparse representation classification. Ann. Methods. 7(6):2367–2372.
   Web of Science ®Google Scholar
• Zhu, T. T., Wu, S., Shao, H. T., Zhang, K. L. and Du, J. L. (2005). Study on organic acids in different original gravity beers. Food Ferment. Ind. 31:108–112.
   Google Scholar