Processing Industry Research Articles

Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence

Background This study aims to empirically test a comprehensive interrelationship between green supply chain management (GSCM), green technology innovation (GTI), waste management (WM), big data analytics capability powered by artificial intelligence (BDAC-AI), and their collective impact on sustainable performance (SP) in organizational contexts. Methods This study was conducted in Pakistan’s food processing sector. The respondents included 495 managers working in the food processing industry. A structural equation modelling (SEM) approach is used to examine direct and indirect relationships between the variables. The originality of this study lies in integration of the technology acceptance model (TAM) and dynamic capability theory (DCT) to understand sustainable practices in the context of the provided model. Results This study highlights that GSCM, GTI, WM, and BDAC-AI have positive, strong, and direct impacts on SP. Furthermore, GTI and WM only partially mediate the link between GSCM and SP, whereas the two moderate the link. In addition, BDAC-AI had a moderating effect on the relationship between GTI and SP. This study has managerial implications, including strategies that involve the use of theoretical frameworks for technological acceptance and dynamic capabilities to support sustainable initiatives. However, it is worth noting that the findings provide a practical contingency for managers and businesses interested in implementing green studies effectively, improving technologies, and strengthening sustainable performance capabilities. Conclusions The study extends the literature by establishing a model for operationalizing GSCM in the food processing sector. Furthermore, it adds value in that it first integrates TAM and DCT to explain sustainable operations and their impact on organizations. Furthermore, it extends the existing literature by establishing a relationship between GSCM and SC. It offers a model through which GSCM can be operationalized in the context of the FS sector.

Assessment of variability for biochemical and mineral contents in fenugreek germplasm

Thirty accessions of fenugreek (Trigonellafoenum-graecumL.) were investigated for the morphological and proximate composition of (moisture, protein, phenols, total antioxidants and mineral contents) during 2022-2023 and 2023-2024. The genotypes showed a wide range of variations in the biochemical parameters. The fully mature leaves of fenugreek germplasm were subjected to biochemical analysis and revealed a wide range of variability in biochemical traits viz. Protein (3.19 to 5.57%) ascorbic acid content (31.8 to 82.5 ug/ g-1), phenol content (0.74 to 2.74 mg GAE g-1) and total antioxidants capacity (0.60 to 5.11 mg Trolox/g). EC 510685 was identified for green- copper color leaf with high antioxidant content of leaves ((5.11 mM Trolox/g), EC510717 was found rich in protein (5.5 %) and Zn content (1.12 mg/100 gm).EC510559 was reported to have higher Ca content (360mg100g-1) while EC510579 was observed with a higher test weight (20.15g). Significant differences were also recorded in mineral content viz. Ca (105 to 360mg/100 gm), Zn (0.34 to 1.2mg/100 gm) and mg (45.7 to 131mg/100 gm). Based on the evaluation superior genotypes identified can be exploited either for the promotion of antioxidants rich fenugreek, coloured leaves genotype for ornamental purposes and minerals-rich genotypes in the processing industry for fortification of fenugreek by developing products with other functional food to overcome malnutrition. This information could also be used to develop antioxidant-rich varieties through a suitable breeding approach.

Evaluation of tuber quality, yield and yield related traits of potato (Solanum tuberosum L.) genotypes at Holetta, Central Ethiopia

In Ethiopia, potato is a food security and income generating crop for millions of smallholder farmers. Previous research efforts related to variety development was focusing on tube yield and late blight resistance with wide adaptability. So far, the potato research program in the country however, devoted less on tuber quality traits and processing parameters except few recently released varieties that considered tuber quality traits for processing. These few potato varieties with processing qualities not yet satisfied the ever-emerging processing industries for chips and French fries in the country. Hence, this study aimed to seek evaluating more potato genotypes that have merits for tuber quality traits used for processing and yield attributes. The experiment was laid out in a randomized completed block design with three replications using 24 potato genotypes to evaluate the processing quality traits of advanced potato genotypes at Holetta Agricultural Research Centre in 2017 main cropping season. The results of the analysis of variance revealed that all the traits showed significant differences among the genotypes indicating there is wide genetic variation. The highest and significantly different tuber physical quality traits of geometric mean diameter and surface area was recorded from CIP395017.229, sphericity of the tuber from CIP396027.205, and length to width ratio from CIP399075.7. The specific gravity of tubers, dry matter content and total starch content also ranged from 1.070 to 1.103 gcm−3, 18.67 to 25.75%, and 12.64% to 18.95%, respectively. The five advanced genotypes CIP398098.65, CIP392617.54, CIP398190.404, CIP394611.112, and C398190.89 were selected for their high total tuber yields over 36.80 t/ha, with more than 31.47 t/ha marketable tuber yield, greeter than 69.92 g/tuber average tuber weight, higher than 58.41 mm3 geometric mean diameter, more than 21.25% dry matter content, excellent tuber characteristics and processing quality traits for chips and French fries making. From this study, selection of potato genotypes that consider both external and internal tuber quality traits would expedite the process of advancing ample potato varieties with processing qualities in the country considering comprehensive study by including more potato genotypes across wider growing environments.

The food industry supply chain: Synergy of environmental orientation and collaboration in implementing circularity

AbstractCircularity within supply chains can be realized through the seamless integration of ecological concerns into business strategies, coupled with stakeholder collaboration throughout the supply chain. A study of a supply chain aiming for circularity was conducted in a real setting for a renowned multinational company in the food processing industry to explore how environmental orientation shapes collaborative practices. The model was applied to reveal the significance of the circular practices that supply chain members follow within the case company. The interpretive ranking process (IRP) was further applied to rank the stakeholders in the supply chain of this company. Results reveal that raw material suppliers, farmers, customers, and volunteers occupy a significant position in implementing circularity in this supply chain. It is essential to provide incentives to suppliers in order to encourage them to adopt practices that promote circular supply chains. Simultaneously, local communities need to play an active role in implementing effective waste management practices. This case study underscores the critical imperative of nurturing a pro‐environmental mindset while fostering synergetic collaboration among stakeholders, thereby paving the path towards achieving circularity.

Combinatorial Green Optimization of Individual Quick Freezer for Energy Savings through Linear Programming and Decision Theory of Shrimp Processing Industry : A Mathematical Approach

Combinatorial Green Optimization of Individual Quick Freezer for Energy Savings through Linear Programming and Decision Theory of Shrimp Processing Industry : A Mathematical Approach

Analysis of difficult flotation mechanisms for coarse low-rank coal: Comparing fluidized-bed and mechanical flotation technologies

Efficient flotation of coarse low-rank coal is essential for enhancing energy utilization in the mineral processing industry. As the beneficiation equipment scales up, the precision of particle size classification before flotation decreases, leading to a higher coarse particle content in the feed, which poses significant challenges to traditional flotation processes. Recent advancements in fluidized bed flotation technology have demonstrated considerable advantages in the flotation of coarse-grained metallic minerals; however, its application to coarse low-rank coal remains relatively unexplored. This study investigates the potential of fluidized bed flotation technology for coarse low-rank coal, employing various analytical methods to elucidate difficult flotation mechanisms. Scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) were utilized to comprehensively characterize the surface morphology and chemical composition of coal samples. The influence of increasing particle size on flotation performance was systematically assessed through measurements of induction time and critical detachment amplitude. Comparative flotation experiments were conducted to evaluate the efficacy of traditional mechanical flotation against fluidized bed flotation technologies. Results indicate that the presence of cracks and oxygen-containing functional groups on the coal surface significantly enhances hydrophilicity, hindering flotation recovery rates. With increasing particle size, the induction time between particles and bubbles lengthens, while the critical detachment amplitude decreases, weakening the attachment and increasing detachment likelihood. Although high-efficiency collectors enhance coal particle hydrophobicity, they cannot fully mitigate the turbulence disrupting bubble-particle interactions in mechanical flotation. Conversely, fluidized bed flotation effectively minimizes turbulence interference, leading to an approximate 40 % increase in the recovery rate of coarse low-rank coal (0.25–1.00 mm) compared to traditional mechanical flotation. This study presents a novel technological approach for the efficient recovery of coarse low-rank coal, contributing to the clean utilization of low-rank coal resources.

A comparative study of RSM and ANN models for predicting spray drying conditions for encapsulation of Lactobacillus casei

AbstractBackground and ObjectivesThe aim of this study was to develop a wall material using pea protein isolate and pectin to optimize the encapsulation of Lactobacillus casei by spray drying. Response surface methodology (RSM) and artificial neural network (ANN) were used to analyze the effect of processing parameters.FindingsThe results showed that both RSM and ANN could be used to successfully characterize the experimental data, although ANN demonstrated greater predictive accuracy than RSM due to a higher R2 and lower mean square error (MSE).ConclusionANN was observed to show more suitability than RSM. The encapsulation efficiency (90.7%), yield (45.5%), and wettability (169 s) of spray‐dried probiotic powder obtained under optimal spray drying conditions (inlet air temperature (132°C); feed flow rate (9.5 mL/min) and pea protein isolate concentration (7.1%)) were observed to be not significantly different (p < .05) from predicted values for all three parameters, demonstrating the validity of applied model.Significance and NoveltyIn this study, production technology of vegan base probiotic powder has been developed using mathematical modeling through the spray‐drying method. Therefore, this data can be useful for food processing industries to develop a high‐quality probiotic powder through spray drying.

Bioprospecting for sustainable and eco-friendly bioproducts: A case study of multi-enzyme production by soil microbes

Microbial multi-enzyme production is a promising research area involving microbes that can simultaneously synthesize numerous enzymes for the degradation of substrates with environmental sustainability and other industrial applications as the focus. During this study, we isolated, screened, and identified some multi-enzyme-producing microbes from three soil sites in Ibadan: Apete Dumpsite (ADS); Botanical Garden University of Ibadan- (BGUI); and Eleyele Cassava Processing Field- (ECPF). Precisely 500 g of soil were collected per point at different depths (0–10, 10–20, and 20–30 cm) and further analyzed. The microbial load and type and enzyme assays were determined. The highest average microbial count (6.9 × 108 CFU/g) was recorded in the ECPF soil sample at 0–10 cm depth, while the lowest microbial count of 3.3 × 105 CFU/g at 20–30 cm soil depth in BGUI. Multi-enzyme-producing microbes were identified phenotypically and molecularly. This study revealed an inverse relationship between microbial community composition and soil depth. Bacterial and fungal genera had higher species richness, biodiversity, and evenness than yeast from all sampled points. With significant amylolytic, proteolytic, lipolytic, cellulolytic, and pectinolytic activities, 22 of the 193 isolates were identified as multienzyme producers. The blasted 16S rRNA and ITS rRNA amplicon sequences compared with sequences in the NCBI gene bank identified Bacillus, Roseomonas, Aeromonas, Phanerochaete, Aspergillus, Fusarium, Trametes, and Rigidoporus species. Results from this study show that topsoils (0–10 cm), rich with organic matter, are good reservoirs of multienzyme-producing microorganisms- applicable for environmental sustainability and in other important industries for waste management, food processing, and biomaterial production.

Detection of foreign materials on Semen Ziziphi Spinosae using hyperspectral imaging technology coupled with convolutional neural networks

The efficacy of Semen Ziziphi Spinosae, a valued medicinal material, can be compromised by the presence of foreign materials, which may also worsen patient conditions and lead to complications. Traditional machine vision techniques often struggle to identify foreign entities that closely resemble Semen Ziziphi Spinosae in color and shape. This study investigates the application of hyperspectral imaging technology for the detection and differentiation of four types of foreign materials (moldy Semen Ziziphi Spinosae shell, wood, and stone) in processing industries. High-resolution hyperspectral imagery was subjected to image segmentation and background noise reduction to separate the contours of the samples within the images. The average spectral signatures of the regions of interest were extracted to construct models such as Convolutional Neural Networks (CNN), Support Vector Machines (SVMs), Mahalanobis distance, Deep Belief Networks, and Generative Adversarial Networks. These models were employed to extract spectral information indicative of foreign matter within the hyperspectral data to identify foreign materialss in Semen Ziziphi Spinosae. Subsequently, the classification outcomes were mapped onto the original hyperspectral images, enabling a visual representation of foreign matter within the seeds. The classification performance of alternative models was compared with that of the CNN model. The results demonstrate that a CNN trained with spectral information from hyperspectral data can accurately identify foreign materialss in Semen Ziziphi Spinosae. The model underwent 30 iterations, achieving an accuracy rate of 99% with a root mean square error (RMSE) of 0.62 for the validation detection, and an accuracy rate of 98% with an RMSE of 0.62 for the validation set.

Environmental benefits of ozonated water for sustainable grapevine disease control: A life cycle and carbon sequestration analysis

The oxidative capacity of ozone makes it a highly effective biocide, widely used in the food and processing industry, as well as in drinking water plants. In a context of tighter restrictions on the use of synthetic plant protection products at the regulatory level, wineries are looking for alternative methods to control diseases, making the application of ozonated water an option to consider. To determine the environmental sustainability of this alternative vineyard disease control treatment, an environmental impact assessment was conducted using the Life Cycle Assessment (LCA) methodology. The assessment was carried out in a vineyard located in the D.O. Rías Baixas of Galicia, located in northwestern Spain. The analysis was conducted on the basis of two functional units: i) 1 kg of grapes and ii) 1 ha of land managed and using a cradle-to-gate boundary system. Ten impact categories were assessed using the ReCiPe method, except for the water scarcity indicator, which was quantified using AWARE.The environmental sustainability of the vineyard was further analyzed by measuring its carbon sequestration potential to obtain a more complete environmental profile. The RothC-26.3 model was chosen to estimate absolute carbon sequestration and annual sequestration rate from 2020 to 2040. Since the ozone-only scenarios lost all harvest, no environmental assessment was performed for such scenarios. For the remaining scenarios, the findings suggest that those using a combination of ozonated water and fungicides have the worst environmental performance due to notable reductions in grape yield and more frequent disease control interventions, particularly in certain scenarios. When evaluating environmental performance per hectare of land managed, the scenario using ozonated water with a limited number of disease control interventions exhibited the most favorable environmental profile, primarily due to reduced use of fungicides and disease control passes. In addition, the main contributors to the vineyard environmental profile identified were diesel fuel combustion during field operations, fertilizer use and production, and irrigation. In addition, the research indicates that the vineyard sequesters carbon annually, but this alone is insufficient to offset its greenhouse gas emissions. However, it is estimated that through the application of more appropriate soil management practices, the vineyard could achieve carbon neutrality and potentially increase soil carbon stocks over time.

Apresentação do Centro de Competência e Desenvolvimento da Economia Azul

The blue economy focuses on sustainable and regenerative development associated with resources and sectors related to the aquatic environment (ocean, seas, rivers and lakes). The blue economy encompasses traditional economic activities such as fishing, aquaculture and processing industries, maritime transport, port facilities and logistics, maritime infrastructure and works, shipbuilding and maintenance, coastal tourism and nautical tourism, sports and culture, and education, training and scientific research. It also includes emerging economic activities such as renewable ocean energies (wind, wave and tidal), marine biotechnology (biofuels, genetic resources, pharmaceuticals), maritime surveillance, carbon capture and sequestration, among many others. Each of these activities faces challenges associated with decarbonisation, digitalisation and the circular economy, while offering vast opportunities for innovation, entrepreneurship, investment and the development of professional careers, boosting the creation of qualified jobs. The blue economy is driven by several critical factors, including the promotion of a healthy and resilient ocean, the protection of biodiversity, the existence and quality of basic sanitation, the creation and maintenance of vibrant ecosystems, and investment in knowledge, education and inclusive social development.

Training of Trainers 4.0 Industri Kecil Menengah Furnitur Untuk Mendorong Transformasi Digital Dengan Teknologi Internet of Things

The furniture industry in Central Java still faces various challenges, especially in adopting advanced technologies such as the Internet of Things and Industry 4.0. Many furniture SMEs still rely on manual or semi-manual production methods, which result in suboptimal product quality and production efficiency, making it difficult to compete in the global market. To address these challenges, the Community Service Program carried out by the Furniture Industry Business Management Study Program at Polytechnic of Furniture Industry and Wood Processing actively played a role through the Training of Trainers activity. This program aims to provide intensive training on the implementation of IoT and Industry 4.0 technologies to furniture SMEs in Central Java, focusing on developing practical skills and fostering digital transformation. Training of Trainers training involved Furniture Industry Business Management lecturers who possess specialized expertise in Industry 4.0 technologies. The training covered both theoretical and practical aspects, allowing participants to not only understand the concepts but also implement them in their work environments. Evaluation results showed a significant increase in the participants' understanding and skills in applying Industry 4.0 and Internet of Things technologies. A tangible example of the success of this training is PT. AST Indonesia, which successfully implemented Industry 4.0 and Internet of Things technologies in its production processes after participating in the Training of Trainers program. This success is concrete evidence that the training provided by Furniture Industry Business Management can contribute significantly to enhancing the competitiveness of furniture SMEs in the digital era, in line with the vision of "Making Indonesia 4.0.

Preface: 13th Ranshofener Leichtmetalltage

Abstract What strategies are available to minimize resource consumption in the light metal industry through recycling and energy efficiency? How can lightweight design make mobility more sustainable? And what role will artificial intelligence play in future processes in the metal processing industry? These and other questions are addressed at the 13th Ranshofener Leichtmetalltage 2024, which take place on the 26th–27th of September 2024 at the Hotel Gut Brandlhof in Saalfelden, Austria, and are organized in accordance with the criteria of the Austrian Ecolabel for Green Events. Under the guiding theme of “Light Metals Innovations for Environmental and Economic Sustainability”, this year’s conference offers an exciting program in three sessions “Digitalization in the Context of Circularity”, “Sustainable Process Development” and “Innovative Light Metals and their Characterization”. The focus is on decarbonization and digitalization in process and material development as well as the material characterization of light metals. A balanced spectrum of international presentations from universities, RTOs and industry provides participants with an up-to-date overview of the latest scientific findings and successful light metal applications. The 13th Ranshofener Leichtmetalltage 2024 are thus clearly focused on decarbonization and digitalization – even in times of diverse challenges. The first session, “Digitalization in the Context of Circularity”, highlights best-practice examples from the aluminium industry, the balance between sustainability and performance in metal processing and the implementation of complex geometries using wire-based additive manufacturing. The second session, “Sustainable Process Development”, is dedicated to the decarbonization and reduced material consumption of industrial processes, for example through GigaCasting, or the use of innovative recycling technologies. The third session, “Innovative Light Metals and their Characterization”, addresses forward-looking topics such as the development of special aluminium alloys for additive manufacturing, the use of old car scrap for innovative alloys and the decarbonization of the magnesium industry. The conference proceedings contain scientific contributions to the conference. We would like to thank all the authors for their high-quality work and the participants of the 13th Ranshofener Leichtmetalltage 2024 for their interest. We wish you stimulating reading and creative impulses! List of Scientific committee, Organizing committee are available in this Pdf.

State and prospects for the development of hemp cultivation in the Russian Federation

Abstract The article discusses the prerequisites for the revival of the hemp industry. The reasons for the reduction in hemp sown areas and the state of hemp production in different years are shown. The characteristics of this crop as a raw material for the processing industry are given. Numerous types of products obtained from hemp during processing are given. The prospects for the further development of hemp through the federal target program “Development of breeding and seed production of industrial crops in the Russian Federation” are considered. Data are presented on the sown areas on the territory of the Russian Federation from 2010 to 2023, on the territory of the Republic of Bashkortostan from 2021 to 2023.

Yeast mycobiome of fruit and vegetable biowastes revealed by culture-dependent and metabarcoding approaches: Screening for the production of succinic acid and single cell oils

Fruit and vegetable by-products and agro-residues from food processing industries and households negatively impact the environment, economy, and society. Yeasts are metabolically efficient at converting food wastes into value-added products such as biopeptides, single-cell oils, single-cell proteins, and organic acids. This work explores the valorization of twelve fruit and vegetable-derived wastes as inexpensive substrates for obtaining high-value products and metabolically efficient yeasts. Culture-dependent and metabarcoding methods provided a comprehensive overview of the yeast mycobiome, revealing 33 common species. From a biobank of 252 isolates obtained from the twelve biowastes, the 31 most promising isolates were evaluated using a synthetic medium that mimicked fruit and vegetable waste. The results showed that the metabolism was mainly directed towards the Krebs cycle, with lower production of single-cell oil compared to organic acids. Kazachstania humilis stood out from the general metabolic heterogeneity, producing 0.9 g/L of succinic acid and 1.4 g/L of acetic acid. Our results demonstrate that these natural isolates can produce high-value compounds at levels comparable to those obtained under optimal conditions for yeast growth.

Batch-to-Batch Differences in Nutritional Quality of Selected Raw and Cooked Pork Offal

With a pressing need to enhance sustainability and reduce waste, harnessing the untapped nutritional wealth buried in by-products of the meat processing industry is more critical than ever. The research was aimed at comparing variation in raw material between batches and establishing a comprehensive database that would serve as a valuable resource for manufacturers seeking to exploit the potential of pork offal. This study is focused on analysing both raw and cooked offal, including liver, heart, kidney, spleen, blood, tongue, esophagus, as well as meat trimmings. Four batches were analysed with regard to their chemical composition, micro- and macro-element content, amino as well as fatty acid profile. The results indicate significant differences in raw material supplies from the same supplier across batches. In our study, all analysed pork offal and meat trimmings exhibited statistically significant variations between individual batches. Furthermore, clustering analysis conducted on the data will be beneficial in optimising recipes and formulations that utilise pork offal. This research may contribute to the sustainable utilisation of meat by-products, waste reduction and overall value enhancement in the meat processing industry.

Experimental investigation on sustainable machining of monel using vegetable oils as cutting fluids and machine learning-based surface roughness prediction

Abstract The material processing industry is anticipated to mitigate environmental degradation. The protocols established by the International Organisation for Standardisation were adhered to. As a result, it would be prudent to investigate the feasibility of minimizing the use of synthetic cutting fluids from the machining process. This study discusses an environmentally-friendly machining technique for turning nickel-based alloy Monel-500, which evaluates four different cooling conditions: dry machining, flood machining, Co-MQL (coconut oil), and Rb-MQL (Rice Bran Oil). These conditions were tested by experimenting with various machining parameters to investigate four aspects of the turning process: surface finish,cutting temperature, tool wear and chip morphology. Rice bran oil is considered eco-friendly compared to synthetic cutting fluids, and employing it in minimum quantity is economical and helps improve the machined workpiece’s surface finish. The investigation has been further extended by applying machine learning algorithms to predict surface roughness, utilising two logical regressions implemented in Python. Among the two machine learning approaches, the random forest regression technique has demonstrated superior results, achieving a prediction accuracy of 99.8%. Consequently, a decision tree has been developed using this regression model to predict the surface roughness. The structured analysis of the decision tree provides more accurate conclusions, offering flexibility in adjusting parameters and expanding options for operation. As a result, the decision tree approach enables the efficient utilisation of production resources and enhances production capacity by making informed choices about cooling methods during the turning process. Rb-MQL has performed better in all aspects than the other three cooling conditions. When comparing machining under dry conditions, flood cooling, Co-MQL, and Rb-MQL (rice bran oil) reduce the tooltip temperature by 39.5%,25.45 and 24.11%, respectively. Rb-MQL reduced surface roughness by 28.23%,43.59 and 60.49% in contrast with machining under dry, flood, and Co-MQL.

Exonic mutations of POU class 1 homeobox 1 are associated with milk pH in high-producing Holstein Friesian cows

Background and Aim: Milk physicochemical properties play essential role in the milk processing industry, which are moderately to highly affected by genetic factors. This study aimed to evaluate the association between single nucleotide polymorphisms in POU class 1 homeobox 1 (POU1F1) and the physicochemical properties of milk in high-producing Holstein Friesian (HF) cows. Materials and Methods: A total of 149 high-producing dairy cows from PT Ultra Peternakan Bandung Selatan was included in this study. The physicochemical properties of milk, including density, freezing point, pH, lactose, solid non-fat, protein, and ash content, were determined. Moreover, three polymorphisms within the exon regions of POU1F1 (c.195G>A, c.300G>T, and c.828G>A) were analyzed using polymerase chain reaction-restriction fragment length polymorphism. The association between these polymorphisms and the physicochemical properties of milk was determined using a mixed-effects model analysis, in which the lactation period was used as a covariate. Results: This study found that two polymorphisms, c.195G>A and c.828G>A, significantly affected the pH of fresh milk. Cows with both the GG genotypes c.195G>A and c.828G>A had lower milk pH values than those with the other genotypes. In addition, a non-significant effect of POU1F1 was observed on the other physicochemical properties of milk. Conclusion: Two POU1F1 polymorphisms determined the pH of fresh milk in the Indonesian HF population. These are potential marker candidates for milk pH that directly affect the development of dairy products in the milk processing industry. Keywords: exonic mutation, Holstein Friesian, milk pH, physicochemical properties, POU1F1.

NUTRITIONAL AND THERAPEUTIC POTENTIAL OF BIOACTIVE COMPOUNDS FROM MELONS: A MINI REVIEW

Fruit wastes are one of the major sources of municipal waste, however they are a source of bioactive useful compounds which possess numerous health benefits. The fruit processing industry generates 10-60% waste or by-products in solid (peel, seed) and liquid (fruit juice) waste. Melon is a prevalent fruit, consumed worldwide, containing significant quantities of by-products (seed, peel, rind) that have been discarded. Melon is an excellent source of biologically active compounds for humans due to its good taste and rich chemical composition. Various bioactive compounds present in fruit waste (seed and rind) of melon is responsible for its numerous biological activities such as anti-inflammatory, antioxidant, anti-diabetic, anti-carcinogenic, and anti-microbial activity.

Utilization of Nile tilapia viscera oil and lipase as a novel and potential feedstock and catalyst for sustainable biodiesel production

The viscera of Nile tilapia, fish processing industry waste, was utilized to extract oil and lipase and used as a low-cost feedstock and catalyst, respectively, for producing biodiesel. The investigation aimed to improve the process of extracting oil from Nile tilapia viscera (NTVO) by a wet rendering method, and to use it for manufacturing biodiesel using Nile tilapia viscera lipase (NTVL)-based catalyst. Heating Nile tilapia viscera waste at 80 °C for 60 min was the best condition for oil separation, with 51.49 % oil recovery and 6.30 % yield. NTVO characteristics suggested the suitability for biodiesel production. Optimizing the synthesis of biodiesel from NTVO utilizing NTVL catalyst was also studied. The catalyst dose of 20 kUnits, reaction temperature of 35 °C, water content of 1 %, reaction period of 18 h, and methanol to oil molar ratio of 4:1 were found to provide the greatest biodiesel yield (96.9 %) (p < 0.05). ATR-FTIR analysis confirmed the conversion of NTVO to biodiesel. The physicochemical properties of biodiesel obtained met the requirement specified by international standards, namely EN 14214 and ASTM D 6751. These findings highlighted the potential of NTVO and NTVL as a substrate and catalyst for biodiesel synthesis, which might alleviate environmental pollution and disposal issues.