Maqui (Aristotelia Chilensis) berry features a unique profile of anthocyanidins that includes high amounts of delphinidin-3-O-sambubioside-5-O-glucoside and delphinidin-3-O-sambubioside and has shown positive effects on fasting glucose and insulin levels in humans and murine models of type 2 diabetes and obesity. The molecular mechanisms underlying the impact of maqui on the onset and development of the obese phenotype and insulin resistance was investigated in high fat diet-induced obese mice supplemented with a lyophilized maqui berry. Maqui-dietary supplemented animals showed better insulin response and decreased weight gain but also a differential expression of genes involved in de novo lipogenesis, fatty acid oxidation, multilocular lipid droplet formation and thermogenesis in subcutaneous white adipose tissue (scWAT). These changes correlated with an increased expression of the carbohydrate response element binding protein b (Chrebpb), the sterol regulatory binding protein 1c (Srebp1c) and Cellular repressor of adenovirus early region 1A–stimulated genes 1 (Creg1) and an improvement in the fibroblast growth factor 21 (FGF21) signaling. Our evidence suggests that maqui dietary supplementation activates the induction of fuel storage and thermogenesis characteristic of a brown-like phenotype in scWAT and counteracts the unhealthy metabolic impact of an HFD. This induction constitutes a putative strategy to prevent/treat diet-induced obesity and its associated comorbidities.

Near infrared hyperspectral imaging (HSI-NIR) is considered a promising technique able to replace timeconsuming, costly and destructive classic methods to predict and classify deoxynivalenol (DON) contaminated wheat kernels or samples by its concentration and level of contamination, respectively. The main objective of the present study was to standardise the HSI-NIR image acquisition method in naturally contaminated whole wheat kernels to obtain a high accuracy method to quantify and classify samples according to DON levels. To confirm the results, wheat samples were analysed by high performance liquid chromatography as the reference method to determine their DON levels. Hyperspectral images for single kernels and whole samples were obtained and spectral data were processed by multivariate analysis software. The initial work revealed that HSI-NIR was able to overcome kernel orientation, position and pixel selection. The subsequent developed Partial Least Squares (PLS) prediction achieved a RMSEP (Root Mean Square Error of Prediction) of 405 µg/kg and 1174 µg/kg for a cross-validated model and an independent set validated model, respectively. Moreover, the classification accuracy obtained by Linear Discriminant Analysis (LDA) was 62.7% for two categories depending on the UE maximum level (1250 µg/kg). Despite of the results are not accurate enough for DON quantification and sample classification, they can be considered a starting point for further improved protocols for DON management.

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Farmers, cereal suppliers and processors demand rapid techniques for the assessment of mould-associated contamination. Deoxynivalenol (DON) is among the most important Fusarium toxins and related to human and animal diseases...

The present study aimed to evaluate the use hyperspectral imaging (HSI)-NIR spectroscopy to predict DON and ergosterol concentration through high-accuracy prediction and classification models to quantify DON and to classify samples according to EU maximum limit (1250 g/kg). To achieve these objectives, a first set of bulk samples was scanned by HSI-NIR and divided into two subsamples in which one was analysed for ergosterol and the other for DON by HPLC. The method was repeated for a second larger set to build prediction and classification models. All the spectra were pretreated and statistically processed by PLSR and LDA. Prediction models presented RMSEP of 1.17 mg/kg and 501 µg/kg for ergosterol and DON, respectively. Classification achieved an encouraging accuracy of 85.4% for an independent validation set of samples. The results confirm that HSI-NIR may be a suitable technique for DON quantification and classification, although ergosterol was inappropriate for DON indirect detection.

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