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This study aims to determine the influence of bruise damage generated from the impact test on the physical, chemical and nutritional responses of tomato fruit.

The impact loading was applied from different heights. The impact energies for 20, 40 and 60 cm drop heights were 129.59, 259.18 and 388.77 mJ, respectively. The injured samples were kept for 48 hours at low (10 °C) and ambient (22 °C) storage temperatures. Weight loss, firmness, color, total soluble solids (TSS), lycopene and carotenoids were measured before the impact test (day 0) and after 48 hours of the impact and storage.

The drop height of 60 cm and storage at 22 °C showed the highest values in the bruised area. The impact from the 60 cm drop height significantly reduced weight, lightness, yellowness, hue, firmness, lycopene and carotenoids, particularly at 22 °C storage condition. Redness (a*) and color index (CI) showed a remarkable increase (p < 0.05) at 22 °C on tomatoes affected from the highest impact level (388.77 mJ) after 48 hours of storage. No pronounced significance was seen between TSS and drop heights. This study has confirmed that tomato bruising for a short-term storage period induces physiological changes at different storage temperature conditions.

The study can confirm the crucial role of inappropriate handling in increasing fresh produce loss within short-term storage. Also, this research can be considered as a guideline for transporters, handlers, processors, distributors and horticulture researchers in the fresh produce supply chain during postharvest operations.

This study aims to investigate the tendency that Malaysian consumers will choose the recombinant collagen-like protein (RCLP) from bacteria as an alternative source of collagen in their consumption and the determinants of the consumer acceptance.

Quantitative approach is adopted in the study. Descriptive analysis and logistic regression are used to analyze primary data collected from a survey.

The likelihood of consumers choosing RCLP as an alternative source of collagen is higher among elderly and females. The choice is significantly influenced by the idea that the collagen should support maqasid al-syariah, approved by national Islamic body, should clear on the ingredients used and could boost local economic activities in the future.

An ethical halal policy should be outlined as the guiding principles to the potential producers of RCLP.

An alternative source of collagen using bacteria as proposed is not yet being introduced in Malaysian industry, and the analysis on probability that consumers will accept this new sourced collagen product is using primary data from survey.

Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.

A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.

The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.

Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

The construction of geographical indications agri-food (GIAF) brands play an important role in rural revitalization in China, this study aims to explore how to jointly promote brand building among multiple parties.

A tripartite game model of the producers, sales operating enterprises, and local governments is constructed to analyze the strategy choice of the parties in the complex system behavior evolution stability, and the simulation analysis of the influence factors of brand construction of GIAF and verify the game result.

(1) Increased government subsidies and supervision costs are beneficial to accelerating variety improvement and quality improvement of agri-food, but it is not conducive to the government, Therefore, it is necessary to ensure that the subsidy and supervision cost is kept within a reasonable range; (2) The dividend distributed to producers by sales operating enterprises play an important role in encouraging producers to improve the quality safety of agri-food, but it must be kept within a reasonable range to avoid discouraging the enthusiasm of sales operating enterprises; (3) Cost reduction, and revenue improvement are also effective ways to cooperate with all parties in brand co-construction.

This study does not consider consumers or logistics companies in the evolutionary game model.

This study proposes innovative policies and suggestions for improvement of the brand co-construction of all parties.

Based on the “Rural Revitalization” initiative, this study enriches research methods about brand value and provides a new perspective for brand value co-construction, and theoretical guidance, and empirical basis for formulating innovation policies and recommendations.

Brand reputation (BR) is one of the most important factors that affect the consumer–brand relationship and give businesses a competitive advantage. Businesses with a strong BR can increase their market shares and product market prices, in addition to gaining a competitive advantage. In order for businesses to have these advantages, they need to know and analyze their consumers. This study aimed to develop an alternative analysis method by using classification algorithms and regression analysis to measure and evaluate the effect of consumers' BR perceptions on their willingness to pay premium prices (WPP).

The research data were collected from 483 participants by the online survey method due to the COVID-19 pandemic. The data were first analyzed with regression analysis, and the effect of BR on WPP was found to be significant. Then, using artificial intelligence (AI) methods that were not used in previous studies, consumers' perceptions of BR and WPP were clustered and classified.

The results revealed the highest and lowest customer groups with BR and WPP and empirically demonstrated that highly accurate practical classification models can be applied to determine strategies in line with these findings.

The model proposed in this study offers an integrated approach by using AI and regression analysis together and tries to fill the gap in the literature in this field. Therefore, the novelty of this study is to quantitatively reveal and evaluate the relationship between BR and WPP by using AI classification algorithms and regression analysis together.

Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework.

Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique.

To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.

The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential.

In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability.

The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).

The purpose of this paper was to study the color change kinetics of lac dye in response to aldehydes, carbon dioxide and other food spoilage metabolites for its potential application in intelligent food packaging.

UV–Vis spectroscopy was used to study the color change of dye solution. Ratio of absorbance of dye solution at 528 nm (peak of ionized form) to absorbance at 488 nm (peak of unionized form) was used to study the color change. Color change kinetics was studied in terms of change in absorbance ratio (A₅₂₈/A₄₈₈) with time using zero and first-order reaction kinetics. Lac dye-based indicator was prepared to validate the result of study for monitoring quality of strawberries.

Lac dye was orange-red in acidic medium and purple in alkaline medium. Color change of dye in response to benzaldehyde followed zero-order reaction kinetics, whereas for carbon dioxide first-order model was found best. No color change of dye solution was observed for alcohols, ketones and sulfur compounds. In the validation part, the color of the indicator label changed from purple to orange when the strawberries spoiled.

The study expands application area for lac dye as sensing reagent in intelligent food packaging for spoilage or ripeness detection of fruits and vegetables.