An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples

A Comprehensive Study for Determination of Free Fatty Acids in Selected Biological Materials: A Review

The quality of oil is highly dependent on its free fatty acid (FFA) content, especially due to increased restrictions on renewable fuels. As a result, there has been a growing interest in free fatty acid determination methods over the last few decades. While various standard methods are currently available, such as the American Oil Chemists Society (AOCS), International Union of Pure and Applied Chemistry (IUPAC), and Japan Oil Chemists' Society (JOCS), to obtain accurate results, there is a pressing need to investigate a fast, accurate, feasible, and eco-friendly methodology for determining FFA in biological materials. This is owing to inadequate characteristics of the methods, such as solvent consumption and reproducibility, among others. This study aims to investigate FFA determination methods to identify suitable approaches and introduce a fresh perspective.

Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields

Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400-1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R2P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R2P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R2P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.