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A Zebra Chip (ZC) disease detection system was developed based on hyperspectral imaging (HSI) to minimise economic losses in the New Zealand potato chip industry. Current detection methods for other than heavily diseased tubers require peeling or cutting of potato tubers. A rapid and non-destructive grading method would be ideal to remove ZC diseased potatoes at line before processing. The spectral signatures from a large population (n = 3352) of commercially sourced potatoes were collected using HSI in the spectral range of 550 nm–1700 nm. Spectral signatures of each potato (i.e. 1767 ZC infected and 1585 healthy potatoes) were extracted by segmentation and morphological operations. A calibration dataset (80% of the total population was randomly selected), with and without pre-processing, was used for modelling using the partial least squares discriminant analysis (PLS-DA). The model performance shows 92% accuracy for ZC potato identification on validation data (20% of total population). Waveband optimisation by variable importance in projection (VIP) method revealed 34 wavebands sensitive to ZC diseased potatoes. This optimum set of wavebands allowed ZC identification with 89% accuracy. The experiments demonstrate the potential of HSI for identification of ZC infected potatoes in whole tuber before processing. Efficient removal of diseased tubers would reduce processing losses and provide a potential opportunity to access export markets for intact tubers. • Hyperspectral imaging (HSI) was used to identify Zebra Chip (ZC) infected potatoes. • PLS-DA models were developed to detect ZC infected potatoes gives 92% accuracy. • Variable important scores were resulted in 34 important wavebands for ZC potatoes. • Potential of HSI for non-destructive ZC diseased potatoes detection was proven. [ABSTRACT FROM AUTHOR]

In the present study, the effects of the ripening stage, processing method, and geographical origin were investigated through the analysis of carobs and derived products by use of high-performance liquid chromatography-electrospray ionization high-resolution mass spectrometry (HPLC-ESI-HRMS), in combination with chemometric analysis. The distribution of secondary metabolites across the fruit was also established through the characterization of the phenolic pattern of different carob parts. It was observed that carob seeds represent a better potential source of polyphenols since significantly higher levels were demonstrated. The content of polyphenols of both pulp and seeds was diminished upon maturation. Therefore, unripe pods were considerably more enriched in polyphenols. Noticeable changes were also observed during carob processing, especially over the thermal treatment of carob products. In regard to the effect of geographical origin, a multivariate statistical approach was employed to study the relationship between the phenolic composition of carobs and their growing regions. • Unripe carobs are more enriched in polyphenols. • Carob seeds exhibit a significantly higher amount of phenolics compared to the pulp. • Product preparation has an important influence on the phenolic composition. • PCA allowed the classification of carobs from the USA and Mediterranean region. • Classification decision tree PLS-DA enabled the discrimination of carob samples. [ABSTRACT FROM AUTHOR]

Creeping thistle ( Cirsium arvensis (L.) Scop.) is a perennial weed that causes yield loss in sugar beet ( Beta vulgaris L.) crops. The weeds are usually mapped for site specific weed management because they tend to grow in patches. Remote sensing techniques have shown promising results in species discrimination and therefore provide potential for weed mapping. In this study we examined the feasibility of high-resolution imaging for sugar beet and thistle discrimination and proposed a protocol to select multispectral camera filters. Spectral samples from sugar beet and thistle were acquired with a field portable spectroradiometer under field conditions and Partial Least Squares Discriminant Analysis (PLS-DA) classification models were developed with 211 and 36 spectral features of 1.56 and 10 nm bandwidths, respectively. The classification rates obtained using these models were regarded as the maximum obtainable. Then, spectral responses of a multi-band camera equipped with the filter configuration proposed by the PLS-DA models were simulated. Finally, a simulation of crop-weed discrimination was made using small unmanned aerial vehicles (UAV)-based multispectral images. More than 95% of the thistles and 89% of the sugar beets were correctly classified when continuous spectral data were used with 1.56 and 10 nm bandwidths. Accuracy dropped to 93% of thistles identified and 84% of sugar beets correctly classified when only the four best bands were used. The validation based on aerial images showed that sugar beets and thistle plants could be discriminated in images if sufficient pure pixels containing leaf spectra were available, that is with spatial resolutions of 6 mm pixel −1 or finer. [ABSTRACT FROM AUTHOR]

This study aimed to quantitatively screen and compare the chemical compounds including protein, lipid, sucrose, total phenolics (TPC), and total titratable acidity (TTA) along with the fatty acid profile of green coffee beans from various geographical origins (China, Indonesia, Ethiopia, Kenya, Guatemala, Honduras, Brazil, and Colombia). Meanwhile, partial least squares discriminant analysis (PLS-DA) was used to investigate whether the analyzed parameters' data can be successfully applied to differentiate the geographical origins of green coffee beans. The levels of protein, lipid, sucrose, TTA, and TPC in all samples were in the range of 13.06–15.98 g 100 g−1, 12.88–16.29 g 100 g−1, 7.30–11.45 g 100 g−1, 231.43–361.21 mL 0.1 mol L−1 NaOH 100 g−1, and 37.56–50.57 mg GAE g−1, respectively. The fatty acid composition was dominated by C 18:2 , C 16:0 , C 18:1, and C 18:0. The major fatty acid group was unsaturated fatty acids (USFA), accounting for 51.66–54.28% of total fatty acids. Statistical analysis showed significant differences in the chemical and fatty acid composition of green coffee beans from eight geographical origins (P ＜ 0.05). Moreover, the PLS-DA results revealed excellent discrimination between the different geographical origins of green coffee beans, highlighting lipid, C 24:0 , C 22:0 , C 18:3 , C 17:0 , C 18:0 , C 20:0 , C 16:0 , and protein as discriminatory features. • Green Arabica coffee beans from eight different geographical origins were analyzed. • A total of eleven fatty acids were detected in all green coffee beans. • PLS-DA score plot shows strong clustering according to geographical origin. • The fatty acid profile can be seen as a potential marker for origin discrimination. [ABSTRACT FROM AUTHOR]