<jats:p>Buffalo-milk-based dairy products provide various health benefits to humans since buffalo milk serves as a rich source of protein, fat, lactose, calcium, iron, phosphorus, vitamin A and natural antioxidants. Dairy products such as Meekiri, Dadih, Dadi and Lassie, which are derived from Artisanal fermentation of buffalo milk, have been consumed for many years. Probiotic potentials of indigenous microflora in fermented buffalo milk have been well documented. Incorporation of certain probiotics into the buffalo-milk-based dairy products conferred vital health benefits to the consumers, although is not a common practice. However, several challenges are associated with incorporating probiotics into buffalo-milk-based dairy products. The viability of probiotic bacteria can be reduced due to processing and environmental stress during storage. Further, incompatibility of probiotics with traditional starter cultures and high acidity of fermented dairy products may lead to poor viability of probiotics. The weak acidifying performance of probiotics may affect the organoleptic quality of fermented dairy products. Besides these challenges, several innovative technologies such as the use of microencapsulated probiotics, ultrasonication, the inclusion of prebiotics, use of appropriate packaging and optimal storage conditions have been reported, promising stability and viability of probiotics in buffalo-milk-based fermented dairy products.</jats:p>

Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.

<jats:p>The development of digital tools based on artificial intelligence can produce affordable and accurate methodologies to assess quality traits and sensory analysis of beers. These new and emerging technologies can also assess new products in a near real-time fashion through virtual simulations before the brewing process. This research was based on the development of specific digital tools (four models) to assess quality traits and sensory profiles of beers produced using sonication and traditional brewing techniques. Results showed that models developed using supervised machine learning (ML) regression algorithms based on near-infrared spectroscopy (NIR) were highly accurate in the estimation of physicochemical parameters (Model 1; R = 0.94; b = 0.91). Outputs from Model 1 were then used as inputs to obtain estimations of the intensity of sensory descriptors (Model 2; R = 0.99; b = 0.98), liking of sensory attributes (Model 3; R = 0.97; b = 0.99), and the classification of fermentation treatments using supervised classification ML algorithms (Model 4; 96% accuracy). These new digital tools can aid craft brewing companies for product development at lower costs and maintain specific quality traits and sensory profiles, creating original styles of beers to get positioned in the market.</jats:p>

<jats:p>Increasing beer quality demands from consumers have put pressure on brewers to target specific steps within the beer-making process to modify beer styles and quality traits. However, this demands more robust methodologies to assess the final aroma profiles and physicochemical characteristics of beers. This research shows the construction of artificial intelligence (AI) models based on aroma profiles, chemometrics, and chemical fingerprinting using near-infrared spectroscopy (NIR) obtained from 20 commercial beers used as targets. Results showed that machine learning models obtained using NIR from beers as inputs were accurate and robust in the prediction of six important aromas for beer (Model 1; R = 0.91; b = 0.87) and chemometrics (Model 2; R = 0.93; b = 0.90). Additionally, two more accurate models were obtained from robotics (RoboBEER) to obtain the same aroma profiles (Model 3; R = 0.99; b = 1.00) and chemometrics (Model 4; R = 0.98; b = 1.00). Low-cost robotics and sensors coupled with computer vision and machine learning modeling could help brewers in the decision-making process to target specific consumer preferences and to secure higher consumer demands.</jats:p>

<jats:p>Grapevine smoke exposure and the subsequent development of smoke taint in wine has resulted in significant financial losses for grape growers and winemakers throughout the world. Smoke taint is characterized by objectional smoky aromas such as “ashy”, “burning rubber”, and “smoked meats”, resulting in wine that is unpalatable and hence unprofitable. Unfortunately, current climate change models predict a broadening of the window in which bushfires may occur and a rise in bushfire occurrences and severity in major wine growing regions such as Australia, Mediterranean Europe, North and South America, and South Africa. As such, grapevine smoke exposure and smoke taint in wine are increasing problems for growers and winemakers worldwide. Current recommendations for growers concerned that their grapevines have been exposed to smoke are to conduct pre-harvest mini-ferments for sensory assessment and send samples to a commercial laboratory to quantify levels of smoke-derived volatiles in the wine. Significant novel research is being conducted using spectroscopic techniques coupled with machine learning modeling to assess grapevine smoke contamination and taint in grapes and wine, offering growers and winemakers additional tools to monitor grapevine smoke exposure and taint rapidly and non-destructively in grapes and wine.</jats:p>

<jats:p>Label concepts, information, logos, figures, and colors of beverages are critical for consumer perception, preference, and purchase intention. This is especially relevant for new beverage products. During social isolation, many sensory laboratories were unable to provide services, making virtual sensory sessions relevant to studying different label concepts and design preferences among consumers. This study proposed a novel virtual sensory system to analyze coffee labels using videoconference, self-reported, and biometric analysis software from video recordings to obtain sensory and emotional responses from 69 participants (power analysis: 1 − β &gt; 0.99) using six different label concepts: (i) fun, (ii) bold, (iii) natural, (iv) everyday, (v) classic, and (vi) premium. The results show that the label concept rated as having the highest perceived quality was premium, presenting significant differences (p &lt; 0.05) compared to all of the other concepts. The least perceived quality score was attributed to the bold concept due to the confronting aroma lexicon (cheese dip), which is supported by previous studies. Furthermore, even though graphics, colors, and the product name can be considered positive attributes, they do not determine perceived quality or purchase intention, which was found for the bold, everyday, and classic concepts. The findings from this study were as expected and are consistent with those from similar publications related to labels, which shows that the proposed virtual method for sensory sessions and biometrics is reliable. Further technology has been proposed to use this system with multiple participants, which could help beverage companies perform virtual sensory analysis of new products’ labels.</jats:p>

<jats:p>Berry cell death (BCD) is linked to the development of flavors and aromas in berries and wine. The BCD pattern and rate within a growing season start at around 90–100 days after anthesis (DAA), and the rate until harvest depends on environmental factors. This study assessed the BCD effects on berry and wine composition from a boutique commercial vineyard in Victoria, Australia, using fluorescent imaging. Results showed differences in wine sensory profiles from the two blocks studied, mainly related to variations in BCD, due to differences in altitude between blocks. Furthermore, two machine learning (ML) models were constructed using near-infrared spectroscopy (NIR) measurements from full berries as inputs and living tissue (LT) and dead tissue (DT) from berries as targets (Model 1). Model 2 was developed using Brix, LT, DT from the east and west sides of canopies as inputs and using 19 sensory descriptors from wines as targets. High correlation and performances were achieved for both models without signs of overfitting (R = 0.94 and R = 0.80, respectively). These models could be used for decision-making purposes as an objective and comprehensive berry maturity assessment obtained in a non-destructive, accurate, and in a real-time fashion close to harvest, to secure specific wine styles.</jats:p>

<jats:p>Foam-related parameters are associated with beer quality and dependent, among others, on the protein content. This study aimed to develop a machine learning (ML) model to predict the pattern and presence of 54 proteins. Triplicates of 24 beer samples were analyzed through proteomics. Furthermore, samples were analyzed using the RoboBEER to evaluate 15 physical parameters (color, foam, and bubbles), and a portable near-infrared (NIR) device. Proteins were grouped according to their molecular weight (MW), and a matrix was developed to assess only the significant correlations (p &lt; 0.05) with the physical parameters. Two ML models were developed using the NIR (Model 1), and RoboBEER (Model 2) data as inputs to predict the relative quantification of 54 proteins. Proteins in the 0–20 kDa group were negatively correlated with the maximum volume of foam (MaxVol; r = −0.57) and total lifetime of foam (TLTF; r = −0.58), while those within 20–40 kDa had a positive correlation with MaxVol (r = 0.47) and TLTF (r = 0.47). Model 1 was not as accurate (testing r = 0.68; overall r = 0.89) as Model 2 (testing r = 0.90; overall r = 0.93), which may serve as a reliable and affordable method to incorporate the relative quantification of important proteins to explain beer quality.</jats:p>

<jats:p>There is an increasing demand for non-dairy probiotic carriers such as fruit and vegetable juices. Probiotic Lactobacillus sanfranciscensis is predominantly used in the bakery industry, and its efficacy in fruit juices has not been studied sufficiently. Additionally, support from the carrier matrices for maintaining probiotic viability and gastrointestinal tolerance is important in selecting suitable vehicles for probiotic delivery. Three different non-dairy carrier juices (apple, orange and tomato) were tested for their ability to maintain L. sanfranciscensis viable during four weeks of refrigerated storage (4 °C). Their potential protection of L. sanfranciscensis against in vitro gastrointestinal digestion was also evaluated. Results indicated that the probiotics viability in all three juice samples met the recommended level for probiotic food (&gt;106–107 cfu/mL) at the end of storage. However, all three juice samples showed a comparatively lower protective effect (p &lt; 0.05) on the viability of L. sanfranciscensis when exposed to simulated gastric juice (pH = 2) at the end of 60 min and simulated intestinal juice with 0.3% (w/v) bile salt (pH = 8) at the end of 240 min exposure. In general, the three tested juices can be regarded as the potential non-dairy based carriers for L. sanfranciscensis. The future research is needed to improve the modification of the probiotic carriers in order to prolong the viability of L. sanfranciscensis during the gastrointestinal digestion.</jats:p>

<jats:p>Apple pomace is a major by-product obtained during apple juice processing. Several million metric tons of apple pomace are estimated to be generated worldwide every year. However, the recovery rate of this by-product is low. Pomace is commonly disposed and thrown away as a waste, which results in environmental problems and even public health hazards. As a by-product of the apple juice processing industries, pomace contains plenty of different varieties of nutritionally important compounds, such as carbohydrates, phenolic compounds, dietary fiber and minerals. These important compounds can be recovered from apple pomace, or there is even a possibility of using apple pomace in the food systems directly or after minimal processing. Therefore, apple pomace can be utilized in food products to improve their health benefits and commercial values. This review focuses on the current food applications and influence of apple pomace on the characteristics of various food products.</jats:p>

<jats:p>Agriculture in Myanmar has substantial development potential given the abundance of land, water, and labor resources in the country. Despite this, agricultural productivity in Myanmar is low and farm incomes are amongst the lowest in Asia. The underperformance of crops and low yield is widely reported to be due to low fertilizer use by smallholders. This study investigated the perceptions of smallholders about fertilizer use for cereal crops by considering their motives and decision making. We reported results of a 600 smallholders’ survey and tested whether the reportedly low fertilizer use by smallholders is generally true for central Myanmar. We compared the fertilizer application timing against recommended “good management practices”. Among the surveyed rice farmers, the average fertilizer applied was much higher than previously reported national average fertilizer rates while the majority of the surveyed maize farmers were found to be applying less than the national recommended rates. With respect to timing, nearly half of the surveyed smallholders were not applying nitrogen at the estimated panicle initiation stage, which is often crucial to increase yield, and the majority (82%) of smallholders were applying phosphorus throughout the growth stages, when earlier applications are desirable. Smallholders may be able to reduce the cost of labor by reducing the number of P applications and avoiding late applications.</jats:p>