Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine

[EN] Product inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyperspectral reflectance imaging in the range of 450¿1040 nm was studied as a non-destructive method to differentiate two cultivars of nectarines with a very similar appearance but different taste. Partial least squares discriminant analysis (PLS-DA) was used to develop a prediction model to distinguish intact fruits of the cultivars using pixel-wise and mean spectrum approaches, and then the model was projected onto the complete surface of fruits allowing visual inspection. The results indicated that mean spectrum of the fruit was the most accurate method, a correct discrimination rate of 94% being achieved. Wavelength selection reduced the dimensionality of the hyperspectral images using the regression coefficients of the PLS-DA model. An accuracy of 96% was obtained by using 14 optimal wavelengths, whereas colour imaging and a trained inspection panel achieved a rate of correct classification of only 57% of the fruits.This work was partially funded by INIA and FEDER funds through project RTA2015-00078-00-00. Sandra Munera thanks INIA for the FPI-INIA grant num. 43 (CPR2014-0082), partially supported by European Union FSE funds. The authors wish to thank Fruits de Ponent (Lleida) for providing the fruit.Munera-Picazo, S.; Amigo, JM.; Aleixos Borrás, MN.; Talens Oliag, P.; Cubero-García, S.; Blasco Ivars, J. (2018). Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control. 86:1-10. https://doi.org/10.1016/j.foodcont.2017.10.037S1108

Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging

The internal quality of intact persimmon cv. Rojo Brillante was assessed trough visible and near infrared hyperspectral imaging. Fruits at three stages of commercial maturity were exposed to different treatments with CO2 to obtain fruit with different ripeness and level of astringency (soluble tannin content). Spectral and spatial information were used for building classification models to predict ripeness and astringency trough multivariate analysis techniques like linear and quadratic discriminant analysis (LDA and QDA) and support vector machine (SVM). Additionally, flesh firmness was predicted by partial least square regression (PLSR). The full spectrum was used to determine the internal properties and later principal component analysis (PCA) was used to select optimal wavelengths (580, 680 and 1050 nm). The correct classification was above 92% for the three classifiers in the case of ripeness and 95% for QDA in the case of astringency. A value of R2 = 0.80 and a ratio of prediction deviation (RPD) of 1.86 were obtained with the selected wavelengths for the prediction of firmness which demonstrated the potential of hyperspectral imaging as a non-destructive tool in the assessment of the firmness, ripeness state and astringency level of Rojo Brillante persimmon.This work has been partially funded by the INIA and FEDER through projects RTA2012-00062-C04-01, RTA2012-00062-C04-03 and RTA2013-00043-C02, GVA through the project AICO/2015/122, the International S&T Cooperation Programs of China (2015DFA71150), and the International S&T Cooperation Program of Guangdong Province, China (2013B051000010). Sandra Munera thanks INIA for the grant FPI-INIA #43 (CPR2014-0082) partially supported by FSE funds.Munera-Picazo, S.; Besada Ferreiro, CM.; Aleixos Borrás, MN.; Talens Oliag, P.; Salvador, A.; Sun, D.; Cubero-García, S.... (2017). Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. Food Science and Technology. 77:241-248. https://doi.org/10.1016/j.lwt.2016.11.063S2412487