Dielectric Spectroscopy of Palm Olein During Batch Deep Frying and Their Relation with Degradation Parameters

A review of different frying oils and oleogels as alternative frying media for fat-uptake reduction in deep-fat fried foods

Purpose

This review aims to examine the potential of oleogels as a frying medium to decrease oil absorption during deep-frying and enhance the nutritional and energy content of foods. By investigating the factors influencing oil incorporation during deep-frying and examining the application of oleogels in this process, we seek to provide insights into using oleogels as an alternative to traditional cooking oils.

Scope

Deep-frying, a widely used cooking method, leads to the retention of large amounts of oil in fried food, which has been associated with health concerns. To address this issue, researchers have investigated various methods to minimize oil absorption during frying. One promising approach is the use of oleogels, which are thermo-reversible, three-dimensional gel networks formed by entrapment of bulk oil with a low concentration (<10% of weight) of solid lipid materials known as oleogelators. This review will focus on the following aspects: a) an overview of deep-fried foods, b) factors influencing oil uptake and underlying mechanisms for oil absorption during deep-frying, c) the characterization and application of different frying oils and their oleogels in deep-fried foods, d) components of the oleogel system for deep-frying, and e) the health impact, oxidative stability, and sensory acceptability of using oleogels in deep-frying.

Key findings

The review highlights the potential of oleogels as a promising alternative frying medium to reduce fat absorption in deep-fried foods. Considering the factors influencing oil uptake during deep-frying, as well as exploring the properties and applications of different frying oils and their oleogels, can result in improved product qualities and heightened consumer acceptance. Moreover, oleogels offer the advantage of lower fat content in fried products, addressing health concerns associated with traditional deep-frying methods. The capacity to enhance the nutritional and energy profile of foods while preserving sensory qualities and oxidative stability positions oleogels as a promising choice for upcoming food processing applications.

Electrical Impedance Spectroscopy for Moisture and Oil Content Prediction in Oil Palm (Elaeis guineensis Jacq.) Fruitlets

The global palm oil industry is targeting an increased oil extraction rate in oil palm milling to meet global demand. This can be achieved through the certification of mills and adherence to bunch grading as part of ensuring that only high-quality and ripe fresh fruit bunches are accepted and processed at all mills. However, the current grading process requires the analysis of oil palm bunches, which is laborious and tedious or prone to error due to human subjectivity. This paper introduces a non-destructive technique to predict the moisture and oil content in oil palm fruitlets using electrical impedance spectroscopy. In total, 90 samples of oil palm fruitlets at different stages of ripeness were acquired. Electrical impedance measurement of each fruitlet was done using electrocardiogram (ECG) electrodes connected to an LCR meter at frequencies of 1 kHz, 10 kHz, 20 kHz, and 100 kHz. The actual oil content in the fruitlets was determined using the Soxhlet extraction method, while the actual moisture content was determined using a standard oven-drying method. The variation of electrical impedance values at each frequency was analyzed. At 100 kHz, the correlation coefficients relating the electrical impedance to the moisture and oil content were around -0.84 and 0.80, respectively. Predictions of the moisture and oil content using linear regression of the impedance measurements at 100 kHz gave RMSE values of 5.85% and 5.71%, respectively. This information is useful for oil palm fruit grading and oil yield production estimation in the palm oil industry.

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

Machine Learning (ML) offers new precision technologies with intelligent algorithms and robust computation. This technology benefits various agricultural industries, such as the palm oil sector, which possesses one of the most sustainable industries worldwide. Hence, an in-depth analysis was conducted, which is derived from previous research on ML utilisation in the palm oil in-dustry. The study provided a brief overview of widely used features and prediction algorithms and critically analysed current the state of ML-based palm oil prediction. This analysis is extended to the ML application in the palm oil industry and a comparison of related studies. The analysis was predicated on thoroughly examining the advantages and disadvantages of ML-based palm oil prediction and the proper identification of current and future agricultural industry challenges. Potential solutions for palm oil prediction were added to this list. Artificial intelligence and ma-chine vision were used to develop intelligent systems, revolutionising the palm oil industry. Overall, this article provided a framework for future research in the palm oil agricultural industry by highlighting the importance of ML.

Quality Assessment of Deep-Frying Palm Oil by Impedimetric Sensing with a Simple and Economic Electrochemical Cell

Quality control of deep-frying oil is a global public health concern. A simple and economic electrochemical chamber composed of two bare screen-printed carbon electrodes (working area: 78.54 × 10² cm²; distance: 0.0055 cm; cell constant: 0.70 × 10^-2 cm^-1) was constructed for precisely acquiring the impedimetric responses of a high-resistance palm oil sample (RSD < 7%, n = 3). Good correlations between the measured impedance data (charge transfer resistance and logarithmic output impedance (Log Z) obtained in the frequency region <0.1 Hz) and the regulatory quality indicators (total polar compounds and acid value) were achieved (R² > 0.97), suggesting that the proposed impedimetric sensing method is useful for accurately assessing the deteriorated condition of repeated frying oil. Applications for rapid screening can also be realized because the measurement times of Log Z at any given perturbation frequency from 0.01-1 Hz were all less than 3 min.

Utilization of novel and rapid techniques for characterization of neem Azadirachta indica seed oil and palm oil blends

The authentication of neem oil and its blending with inexpensive vegetable oil, such as, palm oil is a common practice in the neem oil industry. This study was conducted to investigate the neem kernel (Azadirachta indica) oil (NKO) by blending with palm oil and characterize it by studying its effect on the physicochemical properties, dielectric properties and fatty acid profiles of the blend. Blending significantly influenced the color, dielectric, structural and antimicrobial properties of the virgin oil. The NKO was rich in oleic (44.97%), stearic (21.27%), palmitic (16.88%) and linoleic acids (14.08%). The addition of palm oil into NKO significantly influenced the fatty acids profile , which was further confirmed by the FTIR spectra and the dielectric data. Overall, determination of moisture content, palmitic and stearic acid content, color parameter "a" and dielectric measurements were found to be fastest and precise way to detect the NKO and PO blends.

Physicochemical parameter based estimation of discarding points for frying oil using data interpolation and principal component transformation

Data interpolation and principal component transformation (PCT) were used to compute the discarding points of a frying oil by measuring the physicochemical parameters-acid value, carbonyl value, and total polar compounds. Herein, the discarding point refers to the time point (associated with the value of each physicochemical parameter) at which the frying oil should be discarded. First, a primary visual analysis was performed for the obtained data by using line charts. Second, a curve interpolation method was used to compute the discarding points for each parameter and thus determine the discarding points for the frying oil. At 190, 205, and 220°C, the frying oil reached the discarding points at 22.1, 17.7, and 13 hr, respectively. The discarding area was also visualized on the corresponding surfaces for the originally obtained data and the interpolated data to investigate the discarding points. Third, the PCT was conducted for the three parameters at each temperature; the discarding point estimation for the three parameters could be reduced to the estimation from the first principal component (FPC), thereby simplifying this process. At 190, 205, and 220°C, the frying oil reached the discarding points when the FPCs were 10.4524, 6.2881, and -1.7629 at the time points 22.1, 17.7, and 13 hr, respectively. Finally, a verification experiment revealed that the correlation between the results obtained by our interpolation method or PCT and the verified data was higher than 0.98, which demonstrates the effectiveness of our method.