Discriminant analysis of vegetable oils by thermogravimetric-gas chromatography/mass spectrometry combined with data fusion and chemometrics without sample pretreatment

Insights into flavor formation of braised chicken: Based on E-nose, GC-MS, GC-IMS, and UPLC-Q-Exactive-MS/MS

Effects of braising duration on volatile organic compounds (VOCs) and lipids in chicken were investigated. Aroma profiles identified by an electronic nose were effective in differentiating braising stages. During braising process, a total of 25 key VOCs were detected in braised chicken, and sample braised for 210 min exhibited the highest level of key VOCs. Additionally, a gas chromatography mass spectrometry fingerprint was established to evaluate the distribution of VOCs throughout the braising process. Partial least square discriminant analysis indicated that 2-heptanone, 3-methyl-2-butanone, octanal, nonanal, butanal, (E)-2-pentenal, 1-octen-3-ol, 1-hexanol, pentanal, hexanal, and 1-pentanol significantly affected flavor characteristics of braised chicken. Furthermore, 88 differential lipids were screened, and glycerolipids metabolic was found to be main metabolic pathway during braising process. Triglycerides (TG) and phosphatidyl ethanolamine (PE), such as TG (16:0/18:1/18:2), TG (18:0/18:1/18:2), TG (18:1/18:2/18:3), TG (18:1/18:1/18:2), PE (O-18:2/18:2), PE(O-18:2/18:1), and TG (16:0/16:1/18:2), played a vital role in the generation of VOCs.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.

Evening primrose oil: a comprehensive review of its bioactives, extraction, analysis, oil quality, therapeutic merits, and safety

Oil crops have become increasingly farmed worldwide because of their numerous functions in foods and health. In particular, oil derived from the seeds of evening primrose (Oenothera biennis) (EPO) comprises essential fatty acids of the omega-6 (ω-6) series. It is well recognized to promote immune cells with a healthy balance and management of female ailments. The nutrients of interest in this oil are linoleic acid (LA, 70-74%) and γ-linolenic acid (GLA, 8-10%), which are polyunsaturated fatty acids (PUFA) that account for EPO's popularity as a dietary supplement. Various other chemicals in EPO function together to supply the body with PUFA, elevate normal ω-6 essential fatty acid levels, and support general health and well-being. The inclusive EPO biochemical analysis further succeeded in identifying several other components, i.e., triterpenes, phenolic acids, tocopherols, and phytosterols of potential health benefits. This comprehensive review capitalizes on EPO, the superior product of O. biennis, highlighting the interrelationship between various methods of cultivation, extraction, holistic chemical composition, sensory characters, and medicinal value. Besides the literature review, this study restates the numerous health advantages of primrose oil and possible drug-EPO interactions since a wide spectrum of drugs are administered concomitantly with EPO. Modern techniques to evaluate EPO chemical composition are addressed with emphasis on the missing gaps and future perspectives to ensure best oil quality and nutraceutical benefits.

LC-MS Metabolomic Profiling of Five Types of Unrefined, Cold-Pressed Seed Oils to Identify Markers to Determine Oil Authenticity and to Test for Oil Adulteration

The authenticity of food products marketed as health-promoting foods-especially unrefined, cold-pressed seed oils-should be controlled to ensure their quality and safeguard consumers and patients. Metabolomic profiling using liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (LC-QTOF) was employed to identify authenticity markers for five types of unrefined, cold-pressed seed oils: black seed oil (Nigella sativa L.), pumpkin seed oil (Cucurbita pepo L.), evening primrose oil (Oenothera biennis L.), hemp oil (Cannabis sativa L.) and milk thistle oil (Silybum marianum). Of the 36 oil-specific markers detected, 10 were established for black seed oil, 8 for evening primrose seed oil, 7 for hemp seed oil, 4 for milk thistle seed oil and 7 for pumpkin seed oil. In addition, the influence of matrix variability on the oil-specific metabolic markers was examined by studying binary oil mixtures containing varying volume percentages of each tested oil and each of three potential adulterants: sunflower, rapeseed and sesame oil. The presence of oil-specific markers was confirmed in 7 commercial oil mix products. The identified 36 oil-specific metabolic markers proved useful for confirming the authenticity of the five target seed oils. The ability to detect adulterations of these oils with sunflower, rapeseed and sesame oil was demonstrated.

Characterization of selected commercially available grilled lamb shashliks based on flavor profiles using GC-MS, GC × GC-TOF-MS, GC-IMS, E-nose and E-tongue combined with chemometrics

HS-SPME-GC-MS, SPME-Arrow-GC × GC-TOF-MS, HS-GC-IMS, Electronic-nose, and Electronic-tongue systems were applied in a feasibility study of the flavor characterization of five commercially available Chinese grilled lamb shashliks. A total of 198 volatile organic compounds (VOCs) were identified (∼71% by GC × GC-TOF-MS). Using data fusion strategies, five predictive models were applied to the composition of VOCs and brand identification of the lamb shashliks. Compared with partial least squares regression, support vector machine, deep neural network, and RegBoost modeling, a momentum deep belief network model performed best in predicting VOCs content and identifying shashlik brands (R² above 0.96, and RMSE below 0.1). Intelligent sensory technology combined with chemometrics is a promising approach to the flavor characterization of shashliks and other food matrices.

Enhanced waste hot-pot oil (WHPO) anaerobic digestion for biomethane production: Mechanism and dynamics of fatty acids conversion

Resource depletion and climate changes due to human activities and excessive burning of fossil fuels are the driving forces to explore alternatives clean energy resources. Anaerobic digestion of bio-waste provides a unique opportunity to fulfil this objective through biogas production. The present study aimed to evaluate waste hot-pot oil (WHPO) at different feeding ratios as a novel lipidic waste for anaerobic mono-digestion. The highest recorded maximum biomethane potential (M_max) was 274.1 L kg^-1 VS at 1.2% WHPO, which showed significant differences with those of 0.8% and 1.6% (227.09 and 237.62 L kg^-1 VS, respectively). The changes in volatile fatty acids (VFAs), medium chain fatty acids (MCFAs), and long-chain fatty acids (LCFAs) as intermediates of WHPO decomposition were investigated before and after anaerobic digestion. Results showed efficient production and utilization of VFAs at all studied WHPO ratios, whereas the maximum utilization of VFAs (90-95%) was recorded in the reactors with up to 1.2 %WHPO. Although lipid conversion efficiency decreased by increasing the WHPO ratio, 81.2% lipid conversion efficiency was recorded at the highest applied WHPO treatment, which confirms the potential of WHPO as a promising feedstock for anaerobic digestion. The present results will have major implications towards efficient energy recovery and biochemical management of lipidic-waste through efficient anaerobic digestion.