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Apple (Malus × domestica) is one of the most globally widespread cultivated arboreal fruit species, known both for its taste and nutritional values. Apple peels, in particular, constitute a rich source of antioxidants, among them ascorbic acid, one of the main naturally occurring antioxidants and essential nutritive substance, with well-established beneficial effects. However, its content in apple peels may change due to a variety of factors, such as the chosen cultivars. In order to develop a rapid and non-destructive assessment method by means of near-infrared (NIR) spectroscopy combined with multivariate data analysis, we investigated 27 cultivars of apples harvested at the Laimburg Research Centre for Agriculture and Forestry in South Tyrol (Italy) in 2015. L-ascorbic acid content ranges from 2.68 to 88.95 mg 100 g-1 in fresh apple peels. After 10 d of cold storage and 3 d of shelf life, the non-destructive NIR spectroscopy of both, sunny and shady sides of the apples, was carried out using a fiber optic probe with a wavenumber range of 10,000-4,000 cm-1 and resolution of 4 cm-1. Pretreatment techniques of multiplicative signal correction (MSC) and Savitzky-Golay second derivative were used to remove and reduce the noises and other measurement errors in the spectral data. The interval partial least squares (iPLS) regression and synergy interval partial least squares (siPLS) were used to select spectral region, and genetic algorithm partial least squares (GA-PLS) were further used to remove the interferences from the spectra. The selected spectral region was used to develop models. As a comparison, high performance liquid chromatography (HPLC) with diode array detector (DAD) was employed to measure L-ascorbic acid on the same samples of apple peels. The measurements showed excellent consistency. The predictive performance of model showed rpre of 87.55% and RMSEP of 0.095; the results indicated that the method adopted has a high

Apple (Malus × domestica) is one of the most globally widespread cultivated arboreal fruit species, known both for its taste and nutritional values. Apple peels, in particular, constitute a rich source of antioxidants, among them ascorbic acid, one of the main naturally occurring antioxidants and essential nutritive substance, with well-established beneficial effects. However, its content in apple peels may change due to a variety of factors, such as the chosen cultivars. In order to develop a rapid and non-destructive assessment method by means of near-infrared (NIR) spectroscopy combined with multivariate data analysis, we investigated 27 cultivars of apples harvested at the Laimburg Research Centre for Agriculture and Forestry in South Tyrol (Italy) in 2015. L-ascorbic acid content ranges from 2.68 to 88.95 mg 100 g-1 in fresh apple peels. After 10 d of cold storage and 3 d of shelf life, the non-destructive NIR spectroscopy of both, sunny and shady sides of the apples, was carried out using a fiber optic probe with a wavenumber range of 10,000-4,000 cm-1 and resolution of 4 cm-1. Pretreatment techniques of multiplicative signal correction (MSC) and Savitzky-Golay second derivative were used to remove and reduce the noises and other measurement errors in the spectral data. The interval partial least squares (iPLS) regression and synergy interval partial least squares (siPLS) were used to select spectral region, and genetic algorithm partial least squares (GA-PLS) were further used to remove the interferences from the spectra. The selected spectral region was used to develop models. As a comparison, high performance liquid chromatography (HPLC) with diode array detector (DAD) was employed to measure L-ascorbic acid on the same samples of apple peels. The measurements showed excellent consistency. The predictive performance of model showed rpre of 87.55% and RMSEP of 0.095; the results indicated that the method adopted has a high

This work aims to discriminate milk samples according to their geographical origin, heat treatment, and season of production. This was achieved by combining different techniques, such as isotope ratio mass spectrometry (IRMS), mid- (MIRS) and near-infrared spectroscopies (NIRS), and gas chromatography with flame ionization detector (GC-FID). Milk samples were from North Tyrol (raw milk), South Tyrol (raw milk and high-temperature short time (HTST)), both collected in different seasons. Ultra-high-temperature (UHT) milk samples were from other European regions. These techniques, when used alone, showed limited discrimination capacity. Instead, when such techniques were combined in a multi-variate classification method (PLS-DA), then, milk samples were discriminated according to their geographical origin with an error lower than 5 %. The type of processing and the season were also discriminated. The combination of different techniques compensated their inherent limits and provided a good potential for determining the geographic origin of milk.