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Minced meat is a crucial and widely accepted ingredient used in the preparation of various foods that are widely consumed worldwide. Because of its versatility and handiness, it is used in various dishes, such as emulsion-based meat products, meatballs, sausages, patties, and hamburgers. The ground size has a significant effect on the meat quality attributes. Based on ground size, minced meat can be categorized into three types, viz., fine, medium, and coarse. The present work critically reviewed the impact of ground size on the various quality attributes of meat and meat products. The different ground sizes obtained as a result from different processing methods (such as by using a mincer, grinder, or knife) had a significant impact on the meat quality attributes, including juiciness, flavor, color, pH, tenderness, and sensory evaluation. However, the changes in the meat quality parameters are not only caused by the intensity of the mincing/grinding or the amount of non-intact cells (ANIC), but also by the breakdown of the muscle fibers, structure loss, connective tissues, myofibrillar proteins, and exposure of the surface area. The results of the study revealed that mincing sizes influence the quality parameters such as textural integrity, WHC, color, juiciness, and pH.

Adulteration of food products has become a common problem in many countries. Adulteration may take the form of substitution of one species for another, where the food products from one species have been mixed intentionally with either a similar substitute material or a cheaper species. However, the use of pork and lard is a serious matter in Islam because foods containing ingredients from pig sources are haram (unlawful or prohibited) for Muslims to consume. Conventional techniques such as chromatography are time-consuming and require high capital cost. A possible way out is to use the rapid and less expensive near-infrared (NIR) spectroscopy. Therefore, in this study, apart from the conventional gas chromatography analysis, a variant of NIR spectroscopy, i.e., the long-wave NIR (LW-NIR) spectroscopy system at 1350–2450 nm region in combination with chemometrics analysis was used for detecting and quantifying lard adulteration in palm oil (PO). The result has shown that the samples with a minimum level of adulteration as low as 0.5% could still be easily detected with an overall correct classification rate using linear discriminant analysis (LDA) in the Open-source R software. PLS calibration gives good results between predicted and measured data in quantification, showing linear correlation with coefficient of determination ( R 2 ) at 0.9987. The result agreed well with the gas chromatography-mass spectroscopy (GC–MS) analysis and suggests the feasibility of LW-NIR spectroscopy as an efficient method for detecting and quantifying lard in palm oil.

This study provided a step-by-step procedure to investigate the distribution of 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 the incorporation of principal component analysis (PCA). Dataset pre-processing step, including outlier removal, analysis of variance (ANOVA), dataset adequacy test, dataset transformation and correlation test was performed before the PCA. The method rendered linearity range of 37.5–1000 pmol/µL and accuracy of 85–111% recovery. The bovine and porcine gelatines showed a similar ranking while the l -Alanine (Ala), l -Arginine (Arg) and l -Glutamic acid (Glu) concentrations had differed the fish gelatine from the bovine and porcine gelatines. The PCA, which explained 77.013% cumulative variability at eigenvalue of 5.436, showed AAs with strong FL in PC1 had polar and nonpolar side chains while AAs with strong FL in PC2 had polar side chain. The AAs with moderate and weak FL in PC1 had a nonpolar side chain. The AAs with strong FL of in PC1 were also the same AAs with 7, 6 and 5 strong CMs as determined in the correlation test. The second PCA showed that the l -Serine (Ser), Arg, Glycine (Gly), l -Threonine (Thr), l -Methionine (Met), l -Histidine (His) and L-Hydroxyproline (Hyp) were significant in fish gelatine; Hyp, Met, Thr, Ser, His, Gly, and Arg in bovine gelatine; and l -Proline (Pro), l -Tyrosine (Tyr), l -Valine (Val), l -Leucine (Leu), and l -Phenylalanine (Phe) in porcine gelatine. 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. From this study, the PCA was a useful tool to analyse a multivariate dataset that could provide an in-depth understanding of AA distributions as compared to ANOVA and correlation test.