Prediction of Date Fruit Quality Attributes during Cold Storage Based on Their Electrical Properties Using Artificial Neural Networks Models

Abstract

Evaluating and predicting date fruit quality during cold storage is critical for ensuring a steady supply of high-quality fruits to meet market demands. The traditional destructive methods take time in the laboratory, and the results are based on one specific parameter being tested. Modern modeling techniques, such as Machine Learning (ML) algorithms, offer unique benefits in nondestructive methods for food safety detection and predicting quality attributes. In addition, the electrical properties of agricultural products provide crucial information about the interior structures of biological tissues and their physicochemical status. Therefore, this study aimed to use an alternative approach to predict physicochemical properties, i.e., the pH, total soluble solids (TSS), water activity (aw), and moisture content (MC) of date fruits (Tamar), during cold storage based on their electrical properties using Artificial Neural Networks (ANNs), which is the most popular ML technique. Ten date fruit cultivars were studied to collect data for the targeted parameters at different cold storage times (0, 2, 4, and 6 months) to train and test the ANNs models. The electrical properties of the date fruits were measured using a high-precision LCR (inductance, capacitance, and resistance) meter from 10 Hz to 100 kHz. The ANNs models were compared with a Multiple Linear Regression (MLR) at all testing frequencies of the electrical properties. The MLR models were less accurate than ANNs models in predicting fruit pH and had low performance and weak predictive ability for the TSS, aw, and MC at all testing frequencies. The optimal ANNs prediction model consisted of the input layer with 14 neurons, one hidden layer with 15 neurons, and the output layer with 4 neurons, which was determined depending on the measurements of the electrical properties at a 10 kHz testing frequency. This optimal ANNs model was able to predict the pH with R2 = 0.938 and RMSE = 0.121, TSS with R2 = 0.954 and RMSE = 2.946, aw with R2 = 0.876 and RMSE = 0.020, and MC with R2 = 0.855 and RMSE = 0.803 b by using the measured electrical properties. The developed ANNs model is a powerful tool for predicting fruit quality attributes after learning from the experimental measurement parameters. It can be suggested to efficiently predict the pH, total soluble solids, water activity, and moisture content of date fruits based on their electrical properties at 10 kHz.

Design and Evaluation of a Smart Ex Vitro Acclimatization System for Tissue Culture Plantlets

“…Simple linear regression (y = β 0 + β 1 x) was used to determine the relationship between the sensor measurements (dependent variable) and the observed values (independent variable) after sensor calibration. In addition, the most important evaluation criteria, i.e., the Coefficient of Determination (R 2 ), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) values [60,70], were used to validate the performance of the sensor before using them. The Coefficient of Determination assesses the strong linear relationship between the observed and measured values measured by the standard instrument.…”

Effects of Pomegranate Peel Extract and/or Lactic Acid as Natural Preservatives on Physicochemical, Microbiological Properties, Antioxidant Activity, and Storage Stability of Khalal Barhi Date Fruits

“…Tabikha et al [ 37 ] attributed the decrease in the pH of fruits and vegetables during storage to the conversion of sugars to alcohols and acids through the activity of some microorganisms. In addition, Mohammed et al [ 38 ] reported that the pH of date fruits decreases due to natural changes that occur with the progress of maturity stages, where pH reaches the lowest value in the final stage of ripening. Similarly, Kumar et al [ 29 ] attributed changes in pH and TA during storage to increased respiration rates and enzymatic activity.…”

TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits

“…The pH of the fruit was measured using a pH meter (S400, Mettler-Toledo LLC, Columbus, OH, USA). The TC of the fruits was measured using a spectrophotometer (Genesys 20, Thermo Scientific, Waltham, MA, USA) at 750 nm wavelength based on the method described in [ 36 ]. The TC was determined by establishing a calibration curve by measuring absorbance at different known gallic acid concentrations [ 35 ].…”

“…Major attributes determining the shelf life of dates are pH, total soluble solids (TSS), sugar, MC, water activity (AW), tannin, and firmness. Water activity and moisture content in the fruit maturity stages were analyzed in different modified atmospheric conditions [ 36 ] and verified that the ratio (MC/aw) is 0.33. The non-invasive assessment of fruit firmness remains a “holy grail” in postharvest research [ 13 ].…”