Assessment of physico‐chemical and sensory properties of two date (
            <i>Phoenix dactylifera</i>
            L.) cultivars under commercial cold storage conditions

Noutfia, Younés; Alem, Chakib; Zegzouti, Younes Filali

doi:10.1111/jfpp.14228

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…Majhoul and Boufeggous, whereas TSS was increased in cv. Majhoul after 5 months of storage at 2–4 °C and 66–68.5% RH [ 75 ]. The evaporation of fruit water caused by the relatively high temperature and moderate RH could explain the decrease in both moisture and water activity.…”

Section: Resultsmentioning

confidence: 99%

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

Mohammed

Munir

Aljabr³

2022

Foods

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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.

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Section: Resultsmentioning

confidence: 99%

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

Mohammed

Munir

Aljabr³

2022

Foods

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“…To objectively evaluate the taste quality of pomelo fruit under different storage conditions, a taste sensory evaluation experiment was carried out as previously described by Noutfia et al. (2019) with slight modification. The sensory panel consisted of graduates aged 19–26, five males and five females, who have been trained on pomelo over a period of half years and have participated in a large number of sensory assessments.…”

Section: Methodsmentioning

confidence: 99%

Effects of four storage conditions on the sugar content, acidity, and flavor of “Guanxi” honey pomelo

Wei

Zhang

et al. 2020

J Food Process Preserv

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This research examined the influence of the Pomelo fruit flavor characteristics of four storage methods—room temperature (CK), low‐temperature storage (LTS), modified storage of the atmosphere (MA), and controlled storage atmosphere (CA). The contents, type, and changes in the composition of the taste determining substances have been identified, including sugars, organic acids, and bitter substances. In addition, a sensory assessment was used to calculate the relation between storage and sweetness, acidity, and intensity changes. Results showed the content and change in each taste substance could not be directly related to the taste intensity and fruit flavor. A new taste intensity scale, with a significant correlation between sensory attributes (R2 = .991) was developed for taste assessment. Results indicated that CA storage could effectively control the reduction in sweetness and acidity and reduce the increase in bitterness. Practical applications The taste intensity scale can be used as an indicator for screening and selecting good‐quality storage methods. CA storage technology is a suitable method for improving the taste of stored pomelo fruits.

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“…Majhoul and Boufeggous did not change considerably, whereas TSS increased in cv. Majhoul [65]. The decrease in moisture and water activity could be accounted for by the evaporation of fruit water caused by the relatively high temperature and moderate RH [66].…”

Section: Impact Of Iot-bc On Stored Fruit Characteristicsmentioning

confidence: 99%

Design of a Smart IoT-Based Control System for Remotely Managing Cold Storage Facilities

Mohammed

Riad

Alqahtani

2022

Sensors

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Cold storage is deemed one of the main elements in food safety management to maintain food quality. The temperature, relative humidity (RH), and air quality in cold storage rooms (CSRs) should be carefully controlled to ensure food quality and safety during cold storage. In addition, the components of CSR are exposed to risks caused by the electric current, high temperature surrounding the compressor of the condensing unit, snow and ice accumulation on the evaporator coils, and refrigerant gas leakage. These parameters affect the stored product quality, and the real-time sending of warnings is very important for early preemptive actionability against the risks that may cause damage to the components of the cold storage rooms. The IoT-based control (IoT-BC) with multipurpose sensors in food technologies presents solutions for postharvest quality management of fruits during cold storage. Therefore, this study aimed to design and evaluate a IoT-BC system to remotely control, risk alert, and monitor the microclimate parameters, i.e., RH, temperature, CO2, C2H4, and light and some operating parameters, i.e., the temperature of the refrigeration compressor, the electrical current, and the energy consumption for a modified CSR (MCSR). In addition, the impacts of the designed IoT-BC system on date fruit quality during cold storage were investigated compared with a traditional CSR (TCSR) as a case study. The results showed that the designed IoT-BC system precisely controlled the MCSR, provided reliable data about the interior microclimate atmosphere, applied electrical current and energy consumption of the MCSR, and sent the necessary alerts in case of an emergency based on real-time data analytics. There was no significant effect of the storage time on the most important quality attributes for stored date fruit in the MCSR compared with the TCSR. As a result, the MCSR maintained high-quality attributes of date fruits during cold storage. Based on the positive impact of the designed IoT-BC system on the MCSR and stored fruit quality, this modification seems quite suitable for remotely managing cold storage facilities.

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Assessment of physico‐chemical and sensory properties of two date ( Phoenix dactylifera L.) cultivars under commercial cold storage conditions

Cited by 9 publications

References 28 publications

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

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

Effects of four storage conditions on the sugar content, acidity, and flavor of “Guanxi” honey pomelo

Design of a Smart IoT-Based Control System for Remotely Managing Cold Storage Facilities

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