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Three different vegetable oils, including soybean, corn, and sunflower oils, were differentiated from olive oil by using ultra-performance convergence chromatography coupled with quadrupole time-of-flight (UPC2-QTOF MS) and multivariate data analysis based on their differences in triacylglycerol compositions. Then, olive oil was adulterated by adding these three vegetable oils in 1%, 0.75%, and 0.5% (v/v), and the adulterated olive oils were differentiated from the pure olive oils using the similar analytical strategies but different data processing approaches. After that, the representative markers in differentiating the adulterations were selected, and a mathematical model was created to detect the olive oil adulteration based on these specific markers. These results indicated that UPC2-QTOF MS coupled with multivariate data analysis is a sensitive and accurate method in detecting olive oil adulteration, even in 0.5% adulteration level (v/v). This method could be applied in olive oil adulteration detection, and potentially beneficial to the oil industry.

Seven commercial Chinese chrysanthemum tea products were classified by computer vision combined with machine learning algorithms. Without the need of building any specific hardware, the image acquisition was achieved in two computer vision approaches. In the first approach, a series of multivariate classification models were built after morphological feature extraction of the image. The best prediction accuracies when classifying flowering stages and tea types were respectively 90% and 63%. In comparison, the deep neural network was applied directly on the raw image, yielded 96% and 89% correct identifications when classifying flowering stage and tea type, respectively. The model can be applied for rapid and automatic quality determination of teas and other related foods. The result indicated that computer vision, especially when combined with deep learning or other machine learning techniques can be a convenient and versatile method in the evaluation of food quality., This manuscript reported the multivariate classification of seven commercial Chinese chrysanthemum tea products by computer vision combined with machine learning algorithms. The result indicated that computer vision, especially when combined with deep learning or other machine learning techniques can be a convenient and versatile method in the evaluation of food quality.

Detection of the presence of milk powder in liquid whole milk is challenging due to their similar chemical components. In this study, a sensitive and robust approach has been developed and tested for potential utilization in discriminating adulterated milk from liquid whole milk by analyzing the intact protein and hydrolyzed peptide using ultra‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometer (UPLC‐QTOF‐MS) fingerprints combined with data fusion. Two different datasets from intact protein and peptide fingerprints were fused to improve the discriminating ability of principle component analysis (PCA). Furthermore, the midlevel data fusion coupled with PCA could completely distinguish liquid whole milk from the milk. The limit of detection of milk powder in liquid whole milk was 0.5% (based on the total protein equivalence). These results suggested that fused data from intact protein and peptide fingerprints created greater synergic effect in detecting milk quality, and the combination of data fusion and PCA analysis could be used for the detection of adulterated milk., A sensitive and robust approach has been developed and tested for potential utilization in discriminating adulterated milk from liquid whole milk by analyzing the intact protein and hydrolyzed peptide using UPLC‐QTOF‐MS fingerprints combined with data fusion. The limit of detection of milk powder in liquid whole milk was 0.5% (based on the total protein equivalence). Results suggested that fused data from intact protein and peptide fingerprints created greater synergic effect in detecting milk quality, and the combination of data fusion and principle component analysis (PCA) analysis could be used for the detection of adulterated milk.

The blackberry seed flour was cold‐extracted using 50% acetone and examined for its phytochemical composition and health‐beneficial properties including in vitro gut microbiota modulatory, free radical scavenging, anti‐inflammatory, and antiproliferative capacities. Among identified thirteen components of blackberry seed flour extract through UHPLC‐MS analysis, sanguiin H6 was the primary component and followed by ellagic acid and pedunculagin. For health‐beneficial properties, the blackberry seed flour extract increased the total number of gut bacteria and shifted the abundance of specific bacterial phylum, family, or genus. The extract had RDSC, ORAC, HOSC, and ABTS•+ scavenging capacities of 362, 304, 2,531, and 267 μmol Trolox equivalents (TE)/g, respectively. In addition, the blackberry seed flour extract showed capacities for anti‐inflammation and antiproliferation by suppressing LPS induced IL‐1β mRNA expressions in the cultured J774A.1 mouse macrophages and the proliferation of LNCaP prostate cancer cells. The results suggest potential health benefits and further utilization of blackberry seed flour as functional foods., Blackberry seed flour is a by‐product from oil processing. Investigation of the health‐beneficial components and properties of the blackberry seed flour can lead to its potential utilization in nutraceuticals and functional foods and add value to oil manufacturers and the blackberry producers while reducing environmental contaminations. This study identified the chemical composition of blackberry seed flour using its extract and evaluated potential health‐beneficial properties including in vitro gut microbiota modulatory, free radical scavenging, anti‐inflammatory, and antiproliferative capacities.

3-monochloro-1, 2-propanediol fatty acid esters (3-MCPDEs) comprise a group of food toxicants formed during food processing. 3-MCPDEs have received increasing attention concerning their potential negative effects on human health. However, reports on the toxicity of 3-MCPD esters are still limited. To determine the effects of fatty acid substitutions on the toxicity of their esters, 1-stearic, 1-oleic, 1-linoleic, 1-linoleic-2-palmitic and 1-palmitic-2-linoleic acid esters of 3-MCPD were synthesized and evaluated with respect to their acute oral toxicities in Swiss mice.; Results: 3-MCPDEs were obtained through the reaction of 3-MCPD and fatty acid chlorides, and their purities and structures were characterized by ultraperformance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS), infrared, 1 H and 13 C spectroscopic analyses. Medial lethal doses of 1-stearic, 1-oleic, 1-linoleic, 1-linoleic-2-palmitic and 1-palmitic-2-linoleic acid esters were 2973.8, 2081.4, 2016.3, 5000 and > 5000 mg kg-1 body weight. For the first time, 3-MCPDEs were observed for their toxic effects in the thymus and lung. In addition, major histopathological changes, as well as blood urea nitrogen and creatinine, were examined for mice fed the five 3-MCPDEs.; Conclusion: The results from the present study suggest that the degree of unsaturation, chain length, number of substitution and relative substitution locations of fatty acids might alter the toxicity of 3-MCPDEs. © 2016 Society of Chemical Industry.; © 2016 Society of Chemical Industry.