Green manufacturing practices (GMP) framework for local small and medium enterprises (SME) in Johor, Malaysia: a review on enablers and barriers and preliminary findings on critical factors

This study focuses mainly on Green Manufacturing Practices (GMP) for local small medium enterprises (SME) in the state of Johor, Malaysia. This review includes the enablers and barriers in GMP from previous studies in local and international contexts. Critical factors that affect GMP were also highlighted. It also points out relationships between enablers and barriers of GMP and measures the strengths and weaknesses of GMP. This paper also reviewed the implementation practices, issues and norms. The reviewed outcome will be a guide to formulate an effective framework for GMP in SME

Integrated approach for species identification and quality analysis for Labisia pumila using DNA barcoding and HPLC

Labisia pumila is a precious herb in Southeast Asia that is traditionally used as a health supplement and has been extensively commercialized due to its claimed therapeutic properties in boosting a healthy female reproductive system. Indigenous people used these plants by boiling the leaves; however, in recent years it has been marketed as powdered or capsuled products. Accordingly, accuracy in determination of the authenticity of these modern herbal products has faced great challenges. Lack of authenticity is a public health risk because incorrectly used herbal species can cause adverse effects. Hence, any measures that may aid product authentication would be beneficial. Given the widespread use of Labisia herbal products, the current study focuses on authenticity testing via an integral approach of DNA barcoding and qualitative analysis using HPLC. This study successfully generated DNA reference barcodes (ITS2 and rbcL) for L.pumila var. alata and pumila. The DNA barcode that was generated was then used to identify species of Labisia pumila in herbal medicinal products, while HPLC was utilized to determine their quality. The findings through the synergistic approach (DNA barcode and HPLC) implemented in this study indicate the importance of both methods in providing the strong evidence required for the identification of true species and to examine the authenticity of such herbal medicinal products

Ultrasound assisted extraction and solvent partition for polyhydroxylated and polymethoxylated flavones and phenolic acids from orthosiphon aristatus

The technique of ultrasound-assisted extraction was used to recover polyhydroxylated and polymethoxylated flavones, and phenolic acids from Orthosiphon aristatus. The compounds were then partitioned in different solvents such as hexane, chloroform, ethyl acetate, and acetone with increasing polarity. This workflow would reduce the complexity of chemical composition in crude extract to ease phytochemical profiling using a high throughput analytical tool of liquid chromatography with tandem mass spectrometry. Several newly reported flavone derivatives such as velutin, tricin, 3,7-dimethylquercetin, 4 ',7 '-dimethoxyluteolin, 4 ',7 '-dimethoxyluteolin-dimethyl ether, and 5-hydroxy-3,4 ',7-trimethoxyflavone, in addition to previously reported compounds were detected. The recovered flavone derivatives were mostly lipophilic in nature because they were partitioned in hexane and chloroform, particularly the dimer and trimer of sinensetin. Meanwhile, phenolic acids such as rosmarinic acid and salvianolic acid were detected in the ethyl acetate and acetone fractions, as well as in the remaining aqueous residue. These phenolic acids rich fractions were also found to have higher antioxidant capacity than crude extract. The combination of ultrasound-assisted extraction and solvent partition provided insights into the profile of polyhydroxylated and polymethoxylated flavones, and phenolic acids from the highly complex herbal plant of O. aristatus. The detection of the phytochemicals may explain the previously reported pharmacological properties of the herb

Physiochemical changes and nutritional content of black garlic during fermentation

This study was conducted to understand the reactions involved in black garlic fermentation, and thereby to determine its maturity stage. Black garlic was produced from fresh garlic after fermentation at 70–75 °C and 80–90% relative humidity up to 12 days in this study. The enzymatic hydrolysis of fructans and oligosaccharides, and endophytic bacterial action were likely to be the dominant processes at the initial stage, and followed by non-enzymatic browning Maillard reaction. Fructose was initially detected at the highest concentration and started to decrease after 10 days together with the exponential increase of 5-hydroxymethylfurfural (5-HMF) content. The predominant acids were citric and succinic acids, whereas potassium was the most abundant minerals. The loss of pungent smell could be attributed to the degradation of sulfur containing compounds. The pH of fresh garlic was 6.61, and gradually decreased to 8 days. Garlic fermentation involved multiple reactions including enzymatic hydrolysis, endophytic bacterial action, and non-enzymatic browning reaction to achieve maturity after 8 days

Trypsin hydrolysed protein fractions as radical scavengers and anti-bacterial agents from ficus deltoidea

Different molecular sizes of protein hydrolysates were prepared from the crude protein extract of Ficus deltoidea using the technique of membrane ultrafiltration after trypsin hydrolysis. Gel electrophoretic images shows the presence of 12, 8, 7 and 7 protein bands for the protein fractions prepared from the molecular weight cut-off of 3, 10, 30 and 100 kDa, respectively. The protein hydrolysates were found to have higher radical scavenging activity than those unhydrolysed fractions at the similar molecular size. They exhibited significant differences in the radical scavenging activities based on one-way analysis of variance, except for the protein hydrolysates of 30 and 100 kDa. The smallest protein hydrolysates, 3 kDa appeared to have the comparable activity (30%) with bovine serum albumin as a positive control in this study. Similarly, the 3 kDa protein hydrolysates achieved the highest inhibitory activity (87.5%) against Pseudomonas aeruginosa at the concentration of 128 µg/mL. The protein hydrolysates were found to be more effective against gram negative bacteria (P. aeruginosa and Escherichia coli) because of lower minimum inhibitory concentration (MIC) and effective inhibitory concentration at 50% (EC50) than gram positive bacterium (Staphylococcus aureus). Trypsin catalysed hydrolysis seemed to improve the anti-bacterial activity of protein hydrolysates in a bacterial strain dependent manner. The MIC could achieve 1–55 µg/mL at different molecular sizes of protein fractions. Mass spectra matching revealed that 26% of 226 identified proteins belonged to the category of plant defensive proteins in stress management and metal handling

Molecular identification of Malaysian pineapple cultivar based on Internal Transcribed Spacer (ITS) region

Identification of pineapple cultivar solely based upon traditional method which only based on morphology. However, this approach could results in inaccurate cultivar identification and inconsistent. In this study, we conducted phenetic analysis of 9 Malaysian pineapple cultivars based on DNA sequences of the internal transcribed spacer (ITS) region to evaluate utility of the region as a barcode in identification of pineapple cultivars and to determine phenetic relationships among cultivar. Genomic DNA was directly extracted, and ITS region was amplified and sequenced. Phenetic analysis revealed that the pineapple cultivars used in this study were classified into three groups with high sequence similarity among them. This clearly showed that the DNA barcode from ITS region have good discrimination power to distinguish the pineapple cultivar. Since the tree distinctly separated into three group of cultivar, consensus sequences served as DNA barcode for Malaysian pineapple cultivar can be constructed

Rationale of orthosiphon aristatus for healing diabetic foot ulcer

Orthosiphon aristatus (Blume) Miq. is traditionally used for wound healing in South East Asia and scientifically proven for its antidiabetic potential. Wounds due to diabetes, especially diabetic foot ulcer (DFU), always involve a complicated healing process. The present work aims to review the information on the rationale of the phytochemicals from O. aristatus in promoting DFU healing. The findings showed that the DFU healing potential of O. aristatus was characterized by a reduction in the blood glucose level, mainly attributed to the significant concentration of constituents such as caffeic acid, rosmarinic acid, and sinensetin in the plant extract. These phytochemicals possibly induce insulin secretion and sensitivity, improve the lipid profile, and stimulate glucose uptake. Furthermore, the healing effect may also be contributed to the antioxidant, anti-inflammatory, and antihyperglycemic properties of the plant. The roles of phytochemicals have been systematically postulated in the 4 phases of the healing process. Moreover, no adverse toxic sign or abnormality has been reported upon oral administration of the plant extract. This suggests that O. aristatus extract could be a potential diabetic wound healing phytomedicine for further preclinical and clinical studies

Review on Techniques for Plant Leaf Classification and Recognition

Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extraction appeared to improve the performance of plant leaf classification. The findings of previous studies are critically compared in terms of their accuracy based on the applied neural network techniques. This paper aims to review and analyze the implementation and performance of various methodologies on plant classification. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation