NIR spectroscopy for the optimization of postharvest apple management |
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| Institution: | 1. DeFENS, Department of Food, Environmental and Nutritional Sciences, Università degli Studi di Milano, via Celoria 2, 20133 Milano, Italy;2. Department of Agricultural and Environmental Sciences – Production, Landscape, Agroenergy, Università degli Studi di Milano, via Celoria 2, Milano 20133, Italy;1. Graduate School of Science and Technology, Niigata University, 8050 Ikarashi 2-no-cho, Nishi-ku, Niigata 950-2181, Japan;2. Division of Horticulture, Chiang Mai University, 239 Suthep, Chiang Mai 50200, Thailand;3. Kaisei Co., Ltd., Murakami, Niigata 959-3435, Japan;1. School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China;2. National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing100097, China;3. College of Engineering, China Agricultural University, Beijing 100083, China |
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| Abstract: | Apples can be stored for long time under controlled temperature and atmosphere conditions, and therefore, non-destructive and rapid tools are required to assess fruit quality and to monitor changes during the postharvest period. The aim of this study was to evaluate the feasibility of NIR spectroscopy to optimize postharvest apple management and to follow changes in fruit quality during storage. An FT-NIR system operating in diffuse reflectance in the range 12,500–3600 cm?1 was used to evaluate the physico-chemical (dry matter, soluble solids, colour and firmness) and some nutraceutical characteristics (total phenolics, total flavonoids and antioxidant activity) of ‘Golden Delicious’ apples, which were stored for about six months at 1 °C in controlled atmosphere, over two subsequent years. Spectral data were elaborated by PLS regression and LDA classification techniques. Good correlation models between spectral data and chemical and physical parameters were obtained for soluble solids, a* colour coordinate and firmness (0.81 < R2 < 0.90 in calibration and 0.79 < R2 < 0.89 in cross validation). Even higher correlation values (0.89 < R2 < 0.95 in calibration and 0.86 < R2 < 0.92 in cross validation) were obtained for indexes correlated to the antioxidant capacity of apples. The classification technique Linear Discriminant Analysis was applied to spectral data, in order to discriminate apples on the basis of storage time. Average correct classification was higher than 93% in validation and close to 100% in calibration, indicating high potential of NIR spectroscopy for the estimation of storage time of apple lots. |
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| Keywords: | FT-NIR spectroscopy ‘Golden Delicious’ apples Storage Predictive models Classification models |
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