RESULTS: Samples collected from sampling point 1 (company A) and sampling point 9 (company B) yielded the highest total fungal load (>log 4 CFU g-1 ). The prevalent fungal genera isolated were Aspergillus, Fusarium, and Penicillium spp. Aflatoxin B1 was detected in 8.3% of corn samples, and 7.4% of corn-based poultry feed samples along the feed supply chain, whereas AFs B2 , G1 , and G2 were not detected.

RESULTS: PPF was subjected to alkali and bleach treatment prior to hydrolysis, which successfully removed 54% and 75% of non-cellulosic components (hemicellulose and lignin, respectively). Hydrolysis conditions of 5 h, 15:1 (w/w) resin-to-pulp ratio and 50 °C produced CNC particles of 50-100 nm in length. CNC had a crystallinity index of 42% and appeared rod-like morphologically. CNC-stabilised emulsion had better stability when used in combination with soy lecithin (SL), a well-established, commonly used food stabiliser. Emulsion stabilised by the binary mixture of CNC and SL had droplet size, morphology and physical stability comparable to those of emulsion stabilised using SL.

RESULTS: The mean values of colour features in RGB R m , G m , B m , normalised RGB R nm , G nm , B nm HSV H m , S m , V m , and L*a*b* L m , a m , b m were the best estimator for predicting TSS with R2 ≥ 0.90. All colour channels also showed satisfactory accuracies of R2 ≥ 0.80 in predicting the bioyield force, apparent modulus and mean force. The highest average classification accuracy was obtained using LDA with an average accuracy of more than 82%. The study showed that LDA, LSVM, QDA and QSVM obtained the correct classification of up to 100% for R5, whereas R1, R2, R3 and R4 gave classification accuracies in the range between 83.75-91.85%, 85.6-90.25%, 85.75-90.85% and 77.35-87.15% respectively. This indicates R5 colour information was obviously different from R1-R4. The mean values of the HSV channel indicated the best performance to predict the ripening stages of papaya, compared to RGB, normalised RGB and L*a*b*channels, with an average classification accuracy of more than 80%.

RESULTS: The lysis buffer 2 (LB2) has been shown to be the best lysis buffer for DNA extraction from both raw and processed meat samples comparing to other lysis buffers tested. Hence, the LB2 has been found to be ideal to detect meat and porcine DNAs by real-time PCR using pairs of porcine specific primers and universal primers which amplified at 119 bp fragment and 93 bp fragment, respectively. This assay allows detection as low as 0.0001 ng of DNA. Higher efficiency and sensitivity of real-time PCR via a simplified DNA extraction method using LB2 have been observed, as well as a reproducible and high correlation coefficient (R2  = 0.9979) based on the regression analysis of the standard curve have been obtained.

RESULTS: PSC-AgNPs with an average particle size of 11.68 nm inhibited the growth of the pathogenic yeast C. albicans. Values for minimum inhibitory concentration and minimum fungicidal concentration were 250 and 500 mg L-1 , respectively. TEM images revealed that the average particle size of PSC-AgNPs was 16.8 nm, with the values for zeta potential and the polydispersity index being -8.54 mV and 0.137, respectively. XRD and FTIR spectra showed PSC-AgNPs to have a face-centered cubic crystalline structure. The polysaccharides and amino acid residues present in P. sajor-caju extract were found to be involved in reducing Ag+ to AgNP. Both CaICL1 transcription and ICL protein expression were found to be suppressed in the cells treated with PSC-AgNPs as compared with the control.

RESULTS: Our findings revealed that uncoated alginate microcapsules ruptured upon drying and exhibited low encapsulation efficiency (13.81 ± 2.76%). However, the addition of chitosan successfully provided a more complex and rigid external wall structure to enhance the stability of the microcapsules. By prolonging the crosslinking time from 5 to 30 min and increasing the chitosan concentration from 0.1% to 0.5%, the oil encapsulation efficiency was increased by 28%. Under the right gelation pH (pH 4), the extension of gelation time from 1 to 12 h resulted in an increase in alginate-Ca2+ crosslinkings, thus strengthening the microcapsules.

RESULTS: The moisture, pH and sugar composition of all SBH samples adhered to the Malaysian Kelulut Honey Standard (MS2683:2017) but not to the International Codex Standard (CODEX) for honey. Trehalulose presence in all samples, regardless of geographical area, was predominant alongside fructose and glucose. Only hydroxymethylfurfural (HMF) content and electrical conductivity (EC) results complied with both standards. The principal component analysis biplot showed that the discrimination of SBH according to the five different areas was not feasible, indicating sample homogeneity.

RESULTS: Fourier transform infrared spectral analyses showed that the enzymatic hydrolysis altered functional groups of OPL where a secondary amine was present in the PH. Changes were also observed in the thermal stability where the enthalpy heat obtained for PH (933.93-1142.57 J g-1 ) was much lower than OPL (7854.11 J g-1 ). The results showed that the PH extracted by 8% Alcalase exhibited absolute zeta potential, as well as a high emulsifying activity index (70.64 m2  g-1 of protein) and emulsion stability index (60.58 min). Furthermore, this PH showed higher solubility (96.32%) and emulsifying properties compared to other PHs. It is also comparable with commercial plant proteins, indicating that 8% Alcalase is an optimum concentration for hydrolysis.

RESULTS: The dichloromethane extract of P. crispum exhibited the highest phenolic content (42.31 ± 0.50 mg GAE g(-1) ) and ferric reducing ability (0.360 ± 0.009 mmol g(-1) ) of the various extractions performed. The extract showed DPPH radical scavenging activity with an IC50 value of 3310.0 ± 80.5 µg mL(-1) . Mouse fibroblasts (3T3-L1) pre-treated with 400 µg mL(-1) of the extract showed 50.9% protection against H2 O2 -induced DNA damage, suggesting its potential in cancer prevention. The extract (300 µg mL(-1) ) inhibited H2 O2 -induced MCF-7 cell migration by 41% ± 4%. As cell migration is necessary for metastasis of cancer cells, inhibition of migration is an indication of protection against metastasis.

RESULTS: All aqueous enzymatic extraction (AEE)-based methods generally resulted in oil with better oxidative properties and higher tocopherol retention than the use of solvent. Prior to AEE, boiling pre-treatment deactivated the hydrolytic enzymes and preserved the oil's quality. In contrast, high-pressure processing (HPP) pre-treatment accelerated hydrolytic reaction and resulted in an increase in free fatty acids after 140 days at all temperatures. No significant changes were detected in the oils' iodine values and fatty acid composition. The tocopherol content decreased significantly at both 13 and 25 °C after 60 days in the oil from SE method, and after 120 days in oils from AEE-based methods.

RESULTS: Numerical optimization showed that rice noodles prepared with SPI, 68.32 (g kg-1 of rice flour), MTG, 5.06 (g kg-1 of rice flour) and GDL, 5.0 (g kg-1 of rice flour) gave the best response variables; hardness (53.19 N), springiness (0.76), chewiness (20.28 J), tensile strength (60.35 kPa), and cooking time (5.15 min). The pH, sensory, and microstructure results showed that the optimized rice noodles had a more compact microstructure with fewer hollows, optimum pH for MTG action, and overall sensory panelists also showed the highest preference for the optimized formulation, compared to other samples selected from the numerical optimization and desirability tests.

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.