Rapid Determination of Peroxide Value of Peanut Oils During Storage Based on Terahertz Spectroscopy

Artificial neural network-based shelf life prediction approach in the food storage process: A review

The prediction of food shelf life has become a vital tool for distributors and consumers, enabling them to determine storage and optimal edible time, thus avoiding unexpected food waste. Artificial neural network (ANN) have emerged as an effective, fast and accurate method for modeling, simulating and predicting shelf life in food. ANNs are capable of tackling nonlinear, complex and ill-defined problems between the variables without prior knowledge. ANN model exhibited excellent fit performance evidenced by low root mean squared error and high correlation coefficient. The low relative error between actual values and predicted values from the ANN model demonstrates its high accuracy. This paper describes the modeling of ANN in food quality prediction, encompassing commonly used ANN architectures, ANN simulation techniques, and criteria for evaluating ANN model performance. The review focuses on the application of ANN for modeling nonlinear food quality during storage, including dairy, meat, aquatic, fruits, and vegetables products. The future prospects of ANN development mainly focus on optimal models and learning algorithm selection, multiple model fusion, self-learning and self-correcting shelf-life prediction model development, and the potential utilization of deep learning techniques.

Date seeds (Phoenix Dactylifera L.) valorization: chemical composition of lipid fraction

Abstract This research was aimed to study the lipid fraction of date seeds. Seventeen seeds of date palm varieties and clones were evaluated and assessed for their chemical components and for the properties of the date pits oil. Gas liquid chromatography showed that the main unsaturated fatty acid was oleic acid (46.00 - 50.87%), while the main saturated fatty acid was lauric acid (10.11 - 19.03%) for the cultivars Mentouj Tissgharine (MTN) and Bheir Ingli (KBN) respectively; other fatty acids were also identified. The physicochemical characterization showed an acid value ranging from 0.068 to 1.188%, a specific extinction value equal to (K232: 1.350–2.225; K270: 0.318– 0.521), a peroxide value in the interval (1.059–5.618 meq O2/kg) and an iodine value (41.861–59.980 g Iodine/100 g). The pheophytin content of date seed oils was found within the range from 21.855 to 75.685%. The chemical analysis showed that date seed oil can be useful in cosmetic and food products processing.

Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms

Traditional oil quality measurement is mostly based on chemical indicators such as acid value, peroxide value, and p-anisidine value. This process requires specialized knowledge and involves complex steps. Hence, this study designs and proposes a Sesame Oil Quality Assessment Service Platform, which is composed of an Intelligent Sesame Oil Evaluator (ISO Evaluator) and a Cloud Service Platform. Users can quickly assess the quality of sesame oil using this platform. The ISO Evaluator employs Artificial Intelligence of Things (AIoT) sensors to detect changes in volatile gases and the color of the oil during storage. It utilizes deep learning mechanisms, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to determine and evaluate the quality of the sesame oil. Evaluation results demonstrate that the linear discriminant analysis (LDA) value is 95.13. The MQ2, MQ3, MQ4, MQ7, and MQ8 sensors have a positive correlation. The CNN combined with an ANN model achieves a Mean Absolute Percentage Error (MAPE) of 8.1820% for predicting oil quality, while the LSTM model predicts future variations in oil quality indicators with a MAPE of 0.44%. Finally, the designed Sesame Oil Quality Assessment Service Platform effectively addresses issues related to digitization, quality measurement, supply quality observation, and scalability.

Thermal stability of levopimaric acid and its oxidation products

Biofuels are renewable alternatives to fossil fuels. Levopimaric acid‒base biofuels have attracted increasing attention. However, their stability remains a critical issue in practice. Thus, there is a strong impetus to evaluate the thermal stability of levopimaric acid. Through thermogravimetry (TG) and a custom-designed mini closed pressure vessel test (MCPVT) operating under isothermal and stepped temperature conditions, we investigated thermal oxidation characteristics of levopimaric acid under oxygen atmosphere. Thin-layer chromatography (TLC) and iodimetry were used to measure the hydrogen peroxides generated by levopimaric acid oxidation. A high pressure differential scanning calorimeter (HPDSC) was used to assess hydroperoxide thermal decomposition characteristics. Gas chromatography-mass spectrometry (GC-MS) was used to characterize the oxidation products. The thermal decomposition kinetics of levopimaric acid were thus elucidated, and a high peroxide value was detected in the levopimaric acid. The decomposition heat (QDSC) and exothermic onset temperature (Tonset) of hydroperoxides were 338.75 J g-1 and 375.37 K, respectively. Finally, levopimaric acid underwent a second-stage oxidation process at its melt point (423.15 K), resulting in complex oxidation products. Thermal oxidation of levopimaric acid could yield potential thermal hazards, indicating that antioxidants must be added during levopimaric acid application to protect against such hazardous effects.

Study on Production Technology and Volatile Flavor Analysis of Fragrance Zanthoxylum Seasoning Oil

Dried green pepper and first-grade extracted soybean oil were selected as raw materials to study the effect of the Maillard reaction and cold-pressed compound on the quality of Zanthoxylum seasoning oil and its aroma-enhancing effect. The results showed that the optimal technology was as follows: the ratio of material to liquid was 1:5, the heating temperature was 110 °C, the reaction time was 25 or 30 min, and the addition of reducing sugar was 2%. The optimum ratio of fragrant Zanthoxylum seasoning oil was 1:7 for cold pressing oil and hot dipping oil. Compared with Zanthoxylum seasoning oil, it is based on the Maillard reaction and had a more intense and persistent aroma. The taste of fragrant Zanthoxylum seasoning oil was the best of the three blended oils. The possible types of volatile flavor compounds in the three kinds of Zanthoxylum seasoning oils detected by Heracles II ultra-fast gas phase electronic nose were, respectively, 16, 19, and 15. Among the three kinds of Zanthoxylum seasoning oils, the content of limonene, linalool, Eucalyptol, n-pentane α-Pinene, myrcene, and phellandrene was more abundant, which indicated that olefins and alcohols contributed more to the overall flavor of the three kinds of Zanthoxylum seasoning oils.

Quantification of phosphatides in sunflower oils using a potentiometric e-tongue

Consisting of two fatty acyl groups, phospholipids are a vital part of vegetable oils and the source of essential fatty acids. Moreover, phospholipids influence oxidative and flavor stability and color evolution of vegetable oils, and their quantification has a significant role in the quality assessment of oils. In this study, we proposed a new highly efficient, affordable, environmentally friendly, and simple approach for the evaluation of phospholipid concentrations based on potentiometric multisensor systems coupled with chemometric data processing. Support vector machines, partial least squares, and multiple linear regressions were used to predict phosphatide concentrations based on potentiometric multisensor system responses. Application of multivariate regression tools yielded the following root mean square errors of prediction: 0.005 mg/100 g of oil in the range 0.0-59.4 mg/100 g for refined oils; 0.008 mg/100 g in the range 0.0-100 mg/100 g for low phosphatide oils and 0.24 mg/100 g in the range 100-2270 mg/100 g for high phosphatide oils. This approach can be considered as a rapid and straightforward method to quantify the phosphatides in sunflower oils.

Effect of Thermal Treatment on the Internal Structure, Physicochemical Properties and Storage Stability of Whole Grain Highland Barley Flour

In this study, to improve the processing performance of whole grain highland barley flour (whole grain HB flour), they were prepared by sand-roasting, far-infrared baking, steam explosion, and extrusion, and the effects of on functional properties and storage characteristics were measured. The results indicated that sand-roasting, far-infrared baking, and steam explosion all caused cracks and honeycomb structures in the outer layer and endosperm of the highland barley. The XRD analysis results indicated that highland barley starch treated by far-infrared baking exhibited typical A-type crystal structure, while sand-roasting, steam explosion, and extrusion presented the typical V-type. The results of DSC analysis revealed that the onset temperature (To), peak temperature (Tp), gelatinization enthalpy (ΔH), peak viscosity (PV), trough viscosity (TV), and final viscosity (FV) decreased significantly, while the swelling power, water-holding capacity and oil-holding capacity increased significantly. During the storage period, the moisture content and lipase activity of the whole grain HB flour after thermal treatment remained at a low level; the fatty acid value, peroxide value, and malondialdehyde value increased; finally, the cooked whole grain HB flour was unstable during storage. The functional properties of whole grain HB flour can be improved by steam explosion, and will then have better storage stability.

Effect of Drying Methods on Peanut Quality during Storage

Storage is an important step after peanut harvest and drying. Many factors could affect the peanut quality during storage. The quality change differences of peanut after being dried by solar radiation and at 35℃, 40℃, 45℃, 50℃ during later storage were investigated, including moisture content (MC) and germination percentage (GP) of peanut kernels, acid value (AV), peroxide value (PV), iodine value (IV), vitamin E (VE) content and fatty acid composition (FAC) of extracted peanut oil. And the impact of four storage conditions, air-room temperature (A-RT), air-low temperature (A-LT), vacuum-room temperature (V-RT) and nitrogen-room temperature (N-RT) on peanut quality after 10 months' storage were also studied in this paper. The results revealed that drying conditions had only a little influence on peanut quality during later storage. Peanut dried by solar radiation was more easily oxidized than that dried under other drying conditions. The effects of storage time were much greater. The GP, AV, PV, VE content and FAC, showed significantly changes along with storage. GP and VE content decreased, AV and PV increased, and some linoleic acid was oxidized to oleic acid after 10 months' storage. In addition, A-LT exhibited best performance in keeping peanut quality than A-RT, V-RT and N-RT, which demonstrated that low temperature was more advantageous for peanut storage than controlled atmosphere. These results above would provide useful information and reference for the peanut storage to apply in food industry.