MyMedR

Displaying all 4 publications

Abstract:

Sort:

Recent advances in non-invasive techniques for assessing food quality: Applications and innovations

Jimoh KA, Hashim N

Adv Food Nutr Res, 2025;114:301-352.
PMID: 40155087 DOI: 10.1016/bs.afnr.2024.09.006

The global concern for ensuring the safety and authenticity of high-quality food necessitates continuous advancements in food assessment technologies. While conventional methods of food assessment are accurate and precise, they are also laborious, destructive, time-consuming, energy-intensive, chemical-demanding, and less eco-friendly. Their reliability diminishes when dealing with large numbers of food samples. This chapter explores recent advances in non-invasive technologies for food quality assessment, including spectroscopy, optical imaging, and e-sensors. Enhanced by artificial intelligence (AI), these technologies have shown remarkable capabilities in rapid and accurate food identification, authentication, physical appraisal, early disease detection, chemical analysis, and biochemical evaluation. As a result, non-invasive technology holds the potential to revolutionize food quality assessment and assure food safety at every stage of the food supply chain.
Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Jimoh KA, Hashim N, Shamsudin R, Che Man H, Jahari M

J Sci Food Agric, 2024 Mar 07.
PMID: 38451113 DOI: 10.1002/jsfa.13445

BACKGROUND: Five computational intelligence approaches, namely Gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT) and support vector machine (SVM), were used to describe the evolution of moisture during the dehydration process of glutinous rice. The hyperparameters of the models were optimized with three strategies: Bayesian optimization, grid search and random search. To understand the parameters that facilitate intelligence model adaptation to the dehydration process, global sensitivity analysis (GSA) was used to compute the impact of the input variables on the model output.
RESULT: The result shows that the optimum computational intelligence techniques include the 3-9-1 topology trained with Bayesian regulation function for ANN, Gaussian kernel function for SVM, Matérn covariance function combined with zero mean function for GPR, boosting method for EoT and 4 minimum leaf size for DT. GPR has the highest performance with R2 of 100% and 99.71% during calibration and testing of the model, respectively. GSA reveals that all the models significantly rely on the variation in time as the main factor that affects the model outputs.
CONCLUSION: Therefore, the computational intelligence models, especially GPR, can be applied for an effective description of moisture evolution during small-scale and industrial dehydration of glutinous rice. © 2024 Society of Chemical Industry.
Fulltext Hyperspectral imaging for detection of macronutrients retained in glutinous rice under different drying conditions

Jimoh KA, Hashim N, Shamsudin R, Man HC, Jahari M, Megat Ahmad Azman PN, et al.

Curr Res Food Sci, 2025;10:100963.
PMID: 39817041 DOI: 10.1016/j.crfs.2024.100963

This study detected the macronutrients retained in glutinous rice (GR) under different drying conditions by innovatively applying visible-near infrared hyperspectral imaging coupled with different spectra preprocessing and effective wavelength selection techniques (EWs). Subsequently, predictive models were developed based on processed spectra for the detection of the macronutrients, which include protein content (PC), moisture content (MC), fat content (FC), and ash content (AC). The result shows the raw spectra-based model had a prediction accuracy ( R p 2 ) of 0.6493, 0.9521, 0.4594, and 0.9773 for PC, MC, FC, and AC, respectively. Applying Savitzky Golay first derivatives (SG1D) method increases the R p 2 value to 0.9972, 0.9970, 0.9857 and 0.9972 for PC, MC, FC, and AC, respectively. Using the variable iterative space shrinkage algorithm (VISSA) as EWs reduces the spectral bands by over 60%, and this increases the accuracy of the model (SG1D-VISSA-PLSR) to 100%. Therefore, the developed SGID-VISSA-PLSR can be used to build a smart and reliable spectral system for detecting the macronutrients in GR grains.
Extraction optimization and characterization of durian (Durio zibethinus) rind pectin extracted by subcritical water

Suleiman J, Shamsudin R, Hamzah MH, Basri MSM, Jimoh KA

Food Chem, 2025 May 15;474:143123.
PMID: 39929045 DOI: 10.1016/j.foodchem.2025.143123

The subcritical water extraction (SWE) of pectin from durian rind was optimized using response surface methodology with Box-Behnken experimental design. The FTIR, SEM, and DSC analysis were used to examine the physicochemical, structural, thermal, and functional characteristics of the subcritical water-extracted pectin (SWEP) under optimum conditions and contrasted with the conventional acid-extracted pectin (CAEP). The optimum yield of pectin (5.43 %) was achieved under the temperature of 120 °C, time of 18.5 min, and sieve size of 100 μm. The comparative analysis reveals that the yield of SWEP was ∼2.07 % higher than the CAEP yield (3.36 %). Similarly, the equivalent weight, esterification degree, methoxy concentration, anhydrouronic acid content, water holding capacity, and oil holding capacity of SWEP were consistently higher than the CAEP. Therefore, SWE proved effective for obtaining high-quality pectin from durian rind and offers a simplified, cost-effective, and eco-friendly approach, which makes it a viable method for industrial application.