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Shuhaimi Mustaffa

Bio: Shuhaimi Mustaffa is an academic researcher from Universiti Putra Malaysia. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Papers
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Journal ArticleDOI
10 Feb 2016
TL;DR: In this paper, Gas chromatography-mass spectrometry was successfully used to detect and discriminate butter from adulterated with lard, and results were presented in the form of chromatogram.
Abstract: Butter is high priced product; as a consequence, butter can be subjected for adulteration with low price components such as lard. The presence of lard in any products is not allowed for Muslim and Jewish, therefore, its presence must be identified. Gas chromatography-mass spectrometry was successfully used to detect and discriminate butter from adulterated with lard. Results were presented in the form of chromatogram. Principal component analysis (PCA) was used to interpret the data and provided a good grouping of samples with 55.8% of the variation accounted for by PC 1 and 21.5% were accounted for by PC 2. All the lard containing samples formed a separate group from the samples that were free of lard. This method can be developed into a rapid method for detecting the presence of lard in food samples for Halal authentication.

5 citations


Cited by
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Journal ArticleDOI
TL;DR: Raman spectroscopy coupled chemometrics was employed effectively for quantification of lard fat in butter fat samples with easy, robust, effective, low-cost and reliable application in the quality control of butter.

27 citations

Journal ArticleDOI
TL;DR: In this paper, the impact of zinc oxide (ZnO) nano-particles on the compressive strength of the cement mortar has been investigated and three different models are proposed to predict the strength of cement mortar as analysis of covariance (ANCOVA), neural network (NN), and principal component regression (PCR).
Abstract: In practice, it is very common to estimate the strength of concrete by destructive or by partial non-destructive testing on concrete. However, it is a very challenging task to estimate the correct value of the strength of concrete or cement as it is depending on various factors. The present research work is focussed on the impact of zinc oxide (ZnO) nano-particles on the compressive strength of the cement mortar. To investigate the modified compressive strength of the mortar incorporated with ZnO nano-particles, four different types of mixes were prepared with 0%, 0.25%, 0.5%, and 0.75% of the ZnO nanoparticle by the weight cement, respectively. Experimental results show the enhancement in compressive strength up to 0.5%, later on, strength is slightly decreased. By considering the experimental results of cement strength, three different models are proposed to predict the strength of cement mortar as analysis of covariance (ANCOVA), neural network (NN), and principal component regression (PCR). These models also validate the results of experimentation by showing the optimum results at 0.5% of the addition of ZnO nano-particles. These models are trained and tested in excel programming for thirty-six standard cement specimens. At the end of the work, each model is compared with others. Out of three models, the NN model can predict the reliable results for the compressive strength. However, the PCR model is in second place after the NN model though its value of R2 is lesser than the ANCOVA model. PCR gives less residue as compared to ANCOVA. For the prediction of the strength of mortar, ANCOVA is not so significant as compared to the other two models due to the residuals of ANCOVA models are the largest value, though its R2 value is more than the PCR model.

10 citations

Journal ArticleDOI
TL;DR: In this article, a method using fatty acid-based approach is used to detect lard adulterated in wheat biscuit using chemometrics and machine learning-assisted GCMS.
Abstract: Lard adulteration in food products is undesirable particularly among people with certain diet preference or religious restrictions. Previous attempts on detecting lard in wheat-based biscuits using PCR-based method were inconsistent due to the minute amount of DNA present in lard and entrapment of DNA in the starch matrix. Hence, alternative method using fatty acid–based approach is necessary. The present study aimed to detect lard adulterated in wheat biscuit using chemometrics and machine learning–assisted GCMS. Oil was extracted from the laboratory-prepared wheat biscuits using Soxhlet extraction method, converted to fatty acid methyl ester and analysed using GCMS. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were able to cluster lard, wheat biscuits and lard-adulterated samples based on their fatty acid distribution. Random forest outperformed partial least squares-discriminant analysis (PLS-DA) in sample classification. Feature selection using random forest identified two fatty acids as potential biomarkers. C18:3n6 is proposed as the potential biomarker in discriminating pure wheat biscuits and lard-adulterated biscuits due to its dose-dependent composition with lard addition.

6 citations

Book ChapterDOI
01 Jan 2020
TL;DR: In this paper, Ntakatsane et al. pointed out that increased price and fluctuation in its seasonal availability offers an advantage to the milk fat manufacturers to fraudulently adulterate it with cheaper oils/fats to reduce production costs and increase profit margins.
Abstract: Milk fat has been acclaimed as an indispensable superfood as its nutritional and sensory attributes offer plenty of health benefits (Achaya, 1997). It possesses good flavour, pleasant aroma, high calorific value, besides being a source of valuable nutrients such as fat-soluble vitamins and essential fatty acids. The prices of milk fat have shown upward trend due to the growing demand for it in developed countries which has been attributed to the shift in the opinion of the health concern related to its consumption (OECD/FAO, 2018). International Dairy Federation (IDF) has also noted that over the years there has been a shift in demand from vegetable oil based substitutes to butter and dairy fat due to positive health assessment of milk fat and its sensory properties (IDF, 2018). Increased price and fluctuation in its seasonal availability offers an advantage to the milk fat manufacturers to fraudulently adulterate it with cheaper oils/fats to reduce production costs and increase profit margins. Economic advantage of replacing high-priced fats and oils with low-priced oils without labeling the product accordingly escalates the adulteration of expensive oils and fats such as milk fat. This also poses a risk to human health and decreases its functional value (Ntakatsane, Liu, & Zhou, 2013). Characterization of milk fat for its purity is an absolute necessity in order to ensure a constant well-defined quality. Detection of adulterants in milk fat has always been a challenge because of the variable composition of the triglycerides present. The challenge to detect foreign fats in milk become bigger because of the seasonal, species or breeds related variation in the properties of milk fat. Further, the advent of hydrogenated vegetable oil (HVO) industry in the middle of the twentieth century led to large scale adulteration of milk fat with HVO due to the matching physical properties of both fats. Studies related to the detection and quantification of foreign fats in dairy products have been conducted for many decades and constitute priority areas in many research centers (Fontecha, Mayo, Toledano, & Juarez, 2006; Lipp, 1996; Parodi, 1971; Rebechi, Velez, Vaira, & Perotti, 2016; Timms, 1980).

5 citations