Our success stories

We discriminated H. bakeri, G. thoracica and T. binghami honeys using Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), Partial Least Square-Discriminant Analysis (PLS-DA), and Support Vector Machine (SVM). We identified biomarkers e.g. electrical conductivity, moisture, ash content, 2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-1-cyclohexene-1-acetaldehyde and ethyl 2-(5-methyl-5-vinyltetrahydrofuran-2-yl)propan-2-yl carbonate. See our published manuscript on discriminating different types of honey here:

We investigated the effect of ionic liquid-Graviola fruit pulp extract (IL-GPE) on the metabolomics behaviour of colon cancer (HT29) by using an untargeted GC-TOFMS-based metabolic profiling, incorporated with principal components (PCA) and cluster analyses (CA). The PCA identified 44 metabolites and the CA separated three groups of metabolites. Hence, they revealed an alteration of many metabolic pathways, including amino acid metabolism, aerobic glycolysis, urea cycle and ketone bodies metabolism that contributed to energy metabolism and cancer cell proliferation. See our published manuscript of data analytics on cancer research here:

We studied 17 amino acids (AAs) in 50 fish, 50 bovine and 54 porcine gelatines using Ultra-High-Performance Liquid Chromatography Diode-Array Detector (UHPLC–DAD) with outlier removal, analysis of variance (ANOVA), dataset adequacy test and transformation, correlation test and PCA. The 100% fish, bovine and porcine gelatines accommodated grouping 1, 2 and 3, respectively, which proved that AAs with strong FL (Hyp, His, Ser, Arg, Gly, Thr, Pro, Tyr, Met, Val, Leu and Phe) were the significant AAs and becomes the biomarkers to identify the gelatine source. See our published manuscript here:

We assisted the characterization of L-cysteine sources by using Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) and Raman spectroscopy with the incorporation of PCA. Five distinct groups were successfully differentiated in PCA. The proposed method offers a fast and environmentally friendly approach to distinguish the primary sources of L-cysteine. See our published manuscript here:

Fish oil is a common source of fat in fish feed production. However, there is a tendency to substitute fish oil with other fats such as lard to reduce production costs. Thus, an efficient method for lard detection is highly needed for fish feed’s authenticity. In this study, sn-2 fatty acids (sn-2 FAs) and fatty acid (FA) compositions were incorporated with chemometric techniques namely Principal Component Analysis (PCA), Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA), and Orthogonal Partial Least Square-Regression (OPLS-R) to identify lard adulteration in the fish feeds. The inclusion of sn-2 FAs into PCA model 2 exhibited a preferable variation pattern relative to PCA model 1. The PCA identified C14:0, C18:0, C18:2, C18:3, C20:0 sn-2 C16:0, sn-2 C18:0, sn- 2 C18:1, and sn-2 C18:2 were the most significant FAs to discriminate the fish feeds. The inclusion of sn-2 FA composition improved the OPLS-DA model 2 performance by providing more significant class discrimination between lard-adulterated, and non-adulterated fish feeds as compared to OPLS-DA model 1. The OPLS-DA model 2 identified C18:0, C18:2, C18:3, and sn-2 C16:0 FAs as markers of lard adulteration with an increment in the value of the coefficient of determination (R2) and decrement in the Root Mean Square Error of Estimation (RMSEE) and Root Mean Standard of Cross-Validation (RMSECV) values. The Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron-Artificial Neural Network (MLP-ANN), and internal and external validations corroborated the OPLS-DA model 2 and OPLS-R model 2 performances. Therefore, the incorporation of sn-2 FA and FA compositions coupled with the chemometric techniques had improved the detection and quantification of lard adulteration in fish feeds. See our published manuscript here:

This study aims at authenticating gelatine sources using the incorporation of amino acid (AA) analysis via Ultra- High-Performance Liquid Chromatography Diode-Array Detector (UHPLC-DAD) with forensic food models such as discriminant analysis (DA) and principal component analysis (PCA). The multiple linear regression (MLR), principal component regression (PCR) and partial least square regression (PLSR) were compared to select the best model to (1) quantify the porcine adulterant in non-porcine gelatines and (2) quantify porcine adulterant in fish and bovine gelatine. The method linearity was 37.5–1000 pmol/μL with a determination coefficient (R2) of 0.96–1.00 and a total recovery of 85%–111%. The training, testing and validation datasets were established by the AA of fish, bovine and porcine skin gelatines. The DA had successfully classified 100% fish, bovine and porcine gelatines while the PCA identified dominant AA in each gelatine sources. The discriminating model of non-porcine and porcine gelatines (NPPDM) and discriminating model of fish, bovine and porcine gelatines (FBPDM) had 100% correctly classified (1) non-porcine and porcine gelatines and (2) fish, bovine and porcine gelatines. Using the fish and bovine gelatin (PFBG) training dataset, the PCR model was ineffective in quantifying the porcine adulterant in non-porcine gelatines. The MLR was a better model than PCR to quantify porcine adulterant in non-porcine gelatines using porcine adulterant in fish gelatine (PFG) and porcine adulterant in bovine gelatine (PBG) training datasets due to its lower relative error range and average relative error than of the PCR. Likewise, the PCR model of the PFBG training dataset was ineffective to quantify the porcine adulterant, specifically in fish and bovine gelatine. In contrast, the MLR of PFG and PBG training datasets was the best model for quantifying porcine adulterant in fish and bovine gelatines, respectively. The MLR was ineffective to classify porcine gelatine in marshmallow using the PFG training dataset. However, the MLR had successfully quantified porcine gelatine in marshmallow using PFG and PBG training datasets. Since the training, testing and validation datasets were established by the fish, bovine and porcine skin gelatines, the NPPDM, FBPDM and MLR were best applied for these gelatines. This study anticipated that the regulatory bodies might adopt this approach to establish a standard of authentication of gelatine products. See our published manuscript here:

We have published a new book chapter now!

By applying principal component analysis, we had successfully identified which fatty acids are affected by heating activity. The presence and absence of these fatty acids render their role on the antibacterial potency of Carica papaya seed. See our published book chapter here:

This study was aimed at extraction optimization of antibacterial agents from Carica papaya seed against S. enteritidis, B. cereus, V. vulnificus and P. mirabilis as affected by sonication-assisted extraction (SAE), contact time (CT) and solid-to-solvent ratio (SSR). The principal component analysis (PCA) and individual evaluation approaches identified that no SAE, 8 CT and 1:10 SSR were the best treatments with the highest antibacterial potency. The PCA identified no SAE, 8 CT, and 1:5 SSR as the second-beat treatment. The yield, total phenolic compound (TPC), C18:1n9t and C16:1 free fatty acids (FAs) in no SAE, 8 CT and 1:10 SSR treatment inhibited B. cereus, V. vulnificus and P. mirabilis growths while C21:0 and C15:0 in 30 min SAE, 8 CT and 1:2 SSR inhibited S. enteritidis growth. The yield, TPC, C18:1n9t and C16:1 FAs, and C6:0 and C24:1n9, C20:1, C4:0 and C20:0 FAs had antagonistic effects on B. cereus, V. vulnificus and P. mirabilis growths. The C21:0, C15:0, C6:0 and C13:0, and C23:0, C20:0 and C11:0 FAs had antagonistic effects on S. enteritidis growth. The PCA also denoted that the MIC50 and MIC0 had a higher variation than MIC; hence, the former variables were better to use in PCA. See the publication here:

This study aims to (1) correlate and visualise the Coronavirus disease 19 (COVID-19) pandemic spread via Spearman rank coefficients of network analysis (NA) and (2) predict the cumulative number of COVID-19 confirmed and death cases via support vector regression (SVR) based on COVID-19 dataset in Malaysia between July 2020 to June 2021. The NA indicated increasing connectivity between different states throughout the time frame, revealing the most complex network of COVID-19 transmission in the second quarter of 2021. The SVR model predicted future COVID-19 cases and deaths in Malaysia in the second half of 2021. The study demonstrated that the NA and SVR could provide relatively simple yet valuable artificial intelligence techniques for visualising the degree of connectivity and predicting pandemic risk based on confirmed COVID-19 cases and deaths. The Malaysian health authorities used the NA and SVR model results for preventive measures in highly populated states. See the publication here:

Animal fat plays an important role in processed meat products as it is responsible for improving some physicochemical and sensorial qualities of the final products. However, consumption of high-fat food products is linked to a higher risk of various cardiometabolic diseases such as type 2 diabetes mellitus and cardiovascular diseases. Eggplant has the potential to be used as a fat replacer, but different types of eggplants could produce various results. Thus, this study aimed to produce reduced-fat chicken sausages re-formulated with five different types of eggplants [Round Asian Eggplant (RAE), Pearl Red Eggplant (PRE), Pea Eggplant (PE), Round Black Eggplant (RBE), and Green Thai Eggplant (GTE)] as the fat replacers. The chicken sausages were evaluated for physicochemical and sensorial properties and compared to sausage containing only chicken fat as the control. The RAE, PRE, and PE sausages had the lowest fat content at 4.34%, 6.30% and 7.64%, respectively, thus can be claimed as reduced fat chicken sausages. There were no significant differences among all formulations in terms of ash, moisture, protein, cooking loss, water holding capacity, springiness, and cohesiveness. The sensory analysis revealed that consumers accepted the RAE and PRE sausages compared to the control and the least preferred was PE. This was supported by the PCA, which positively proposed lower fat content (4.34%) and higher a* value (3.21) while rejecting higher pH (6.35) and b* values (15.88) of the reduced-fat-chicken sausages. In conclusion, eggplants can be used as fat replacers to produce reduced-fat chicken sausages with Round Asian Eggplant being the best option. See the publication here:

Cultivation location, maturity levels, and extraction solvents could affect the bioactive compounds and biological activities of mulberry (Morus alba Linnaeus). The lack of study on Malaysia- grown mulberry causes its underutilization. This study investigated the bioactive compound content and the antioxidant activity of Sabah-grown mulberry at two different maturity stages (fruits: red mature and black fully ripe; leaves: young and mature) extracted using 70% (v/v) methanol, 60% (v/v) ethanol, and 65% (v/v) acetone. Analyses showed that mulberry fruits demonstrated maturity- dependent increment (except UHPLC-DAD quantification), while the leaves revealed maturity- dependent reduction. Principal component analysis (PCA) displayed 65% (v/v) acetone black fully ripe fruits as the best phenolics and antioxidant sources. However, the 60% (v/v) ethanol black fully ripe fruits contained 20.08–68.43% higher total anthocyanins. Meanwhile, the 65% (v/v) acetone and 70% (v/v) methanol red mature fruits were higher in chlorogenic acid (27.53–47.12%) and rutin (31.42–35.92%) than other fruit extracts, respectively. For leaves, 65% (v/v) acetone young leaves were the best phenolics and antioxidant sources. However, the 60% (v/v) ethanol young leaves possessed greater chlorogenic acid (19.56–74.11%) than other leaf extracts. Overall, Malaysia-grown mulberry is rich in phenolics and antioxidants, suggesting its potential application in food and pharmaceutical products. See the publication here:

This study authenticated fish feed sources and determined lard adulteration using dataset pre-processing, principal component analysis (PCA), discriminant analysis (DA) and partial least square regression (PLSR) on 19 triacylglycerols (TAGs) and 16 thermal properties (TPs). At cumulative variability (90.625%) and Keiser-Meyer Olkin (KMO) value (0.811), the PCA identified 10 TAGs and 3 TPs with strong factor loading. The dioleoyl-1-palmitoyl glycerol (POO), dipalmitoyl-3-oleoyl glycerol (PPO) and dipalmitoyl-1-linoleoyl (PPL) characterized fish feeds containing palm oil while dilinoleoyl-1-oleoyl glycerol (OLL), dilinoleoyl-1-palmitoyl (PLL), dioleoyl-3-linoleoyl glycerol (OOL), initial cooling temperature (ICT), palmitoyl-oleoyl-linoleoyl glycerol (POL), palmitoyl-stearoyl-oleoyl glycerol (PSO) and final heating temperature (FHT) characterized lard-containing fish feeds. The DA had successfully classified the fish feed sources and selected the PPL, POL, PPO, OOL, ICT, PLL, FHT, POO and OLL as the most influential biomarkers for the authentication purpose. The T-test result (p > 0.05) indicated that the PLSR could determine the percentage of lard adulteration in fish feed. Hence, incorporating multivariate and instrumental analyses could authenticate the fish feed sources. See the publication here: