LC-ESI-QTOF-MS/MS Identification and Characterization of Phenolic Compounds from Leaves of Australian Myrtles and Their Antioxidant Activities.

Phenolic compounds, present in plants, provide substantial health advantages, such as antioxidant and anti-inflammatory properties, which enhance cardiovascular and cognitive well-being. Australia is enriched with a wide range of plants with phytopharmacological potential, which needs to be fully elucidated. In this context, we analyzed leaves of aniseed myrtle (Syzygium anisatum), lemon myrtle (Backhousia citriodora), and cinnamon myrtle (Backhousia myrtifolia) for their complex phytochemical profile and antioxidant potential. LC-ESI-QTOF-MS/MS was applied for screening and characterizing these Australian myrtles' phenolic compounds and the structure-function relation of phenolic compounds. This study identified 145 and quantified/semi-quantified 27 phenolic compounds in these Australian myrtles. Furthermore, phenolic contents (total phenolic content (TPC), total condensed tannins (TCT), and total flavonoids (TFC)) and antioxidant potential of phenolic extracts from the leaves of Australian myrtles were quantified. Aniseed myrtle was quantified with the highest TPC (52.49 ± 3.55 mg GAE/g) and total antioxidant potential than other selected myrtles. Catechin, epicatechin, isovitexin, cinnamic acid, and quercetin were quantified as Australian myrtles' most abundant phenolic compounds. Moreover, chemometric analysis further validated the results. This study provides a new insight into the novel potent bioactive phenolic compounds from Australian myrtles that could be potentially useful for functional, nutraceutical, and therapeutic applications.

Bioaccessibility of Phenolic Compounds, Resistant Starch, and Dietary Fibers from Australian Green Banana during In Vitro Digestion and Colonic Fermentation.

Green bananas contain a substantial amount of resistant starch (RS), dietary fiber (DF), and phytochemicals, which exhibit potent antioxidant capabilities, primarily attributable to the abundance of polyphenols. The objective of this study was to assess the variations in the contents and bioaccessibility of RS, DF, and phenolic compounds in three types of Australian green bananas (Cavendish "Musa acuminata", Ladyfinger "Musa paradisiaca L.", and Ducasse "Musa balbisiana"), along with their antioxidant capacities, and the production of short-chain fatty acids (SCFAs) following in vitro simulated gastrointestinal digestion and colonic fermentation. The studied cultivars exhibited significant levels of RS, with Ladyfinger showing the greatest (49%). However, Ducasse bananas had the greatest DF concentration (38.73%). Greater TPC levels for Ladyfinger (2.32 mg GAE/g), as well as TFC and TTC (0.06 mg QE/g and 3.2 mg CE/g, respectively) in Cavendish, together with strong antioxidant capacities (DPPH, 0.89 mg TE/g in Cavendish), have been detected after both intestinal phase and colonic fermentation at 12 and 24 h. The bioaccessibility of most phenolic compounds from bananas was high after gastric and small intestinal digestion. Nevertheless, a significant proportion of kaempferol (31% in Cavendish) remained detectable in the residue after colonic fermentation. The greatest production of SCFAs in all banana cultivars was observed after 24 h of fermentation, except valeric acid, which exhibited the greatest output after 12 h of fermentation. In conclusion, the consumption of whole green bananas may have an advantageous effect on bowel health and offer antioxidant characteristics.

Probiotic Yoghurt Enriched with Mango Peel Powder: Biotransformation of Phenolics and Modulation of Metabolomic Outputs after In Vitro Digestion and Colonic Fermentation.

This study investigated the health-promoting effects and prebiotic functions of mango peel powder (MPP) both as a plain individual ingredient and when incorporated in yoghurt during simulated digestion and fermentation. The treatments included plain MPP, plain yoghurt (YA), yoghurt fortified with MPP (YB), and yoghurt fortified with MPP and lactic acid bacteria (YC), along with a blank (BL). The identification of polyphenols in the extracts of insoluble digesta and phenolic metabolites after the in vitro colonic fermentation were performed employing LC-ESI-QTOF-MS2. These extracts were also subjected to pH, microbial count, production of SCFA, and 16S rRNA analyses. The characterisation of phenolic profiles identified 62 phenolic compounds. Among these compounds, phenolic acids were the major compounds that underwent biotransformation via catabolic pathways such as ring fission, decarboxylation, and dehydroxylation. Changes in pH indicated that YC and MPP reduced the media pH from 6.27 and 6.33 to 4.50 and 4.53, respectively. This decline in pH was associated with significant increases in the LAB counts of these samples. The Bifidobacteria counts were 8.11 ± 0.89 and 8.02 ± 1.01 log CFU/g in YC and MPP, respectively, after 72 h of colonic fermentation. Results also showed that the presence of MPP imparted significant variations in the contents and profiles of individual short chain fatty acids (SCFA) with more predominant production of most SCFA in the MPP and YC treatments. The 16s rRNA sequencing data indicated a highly distinctive microbial population associated with YC in terms of relative abundance. These findings suggested MPP as a promising ingredient for utilisation in functional food formulations aiming to enhance gut health.

Screening and Characterization of Phenolic Compounds from Selected Unripe Fruits and Their Antioxidant Potential.

The food sector's interest in sustainability and the demand for novel bioactive compounds are increasing. Many fruits are wasted every year before ripening due to various climatic conditions and harsh weather. Unripe mangoes, grapes, and black lemons could be rich sources of phenolic compounds that need to be fully elucidated. Using fruit waste as a source of bioactive chemicals has grown increasingly appealing as it may have significant economic benefits. Polyphenols are beneficial for human health to inhibit or minimize oxidative stress and can be used to develop functional and nutraceutical food products. In this context, this study aimed to characterize and screen unripe mangoes, grapes, and black lemons for phenolic compounds using LC-ESI-QTOF-MS/MS and their antioxidant activities. Unripe mangoes were quantified with higher total phenolic content (TPC, 58.01 ± 6.37 mg GAE/g) compared to black lemon (23.08 ± 2.28 mg GAE/g) and unripe grapes (19.42 ± 1.16 mg GAE/g). Furthermore, unripe mangoes were also measured with higher antioxidant potential than unripe grapes and black lemons. A total of 85 phenolic compounds (70 in black lemons, 49 in unripe grapes, and 68 in unripe mango) were identified, and 23 phenolic compounds were quantified using LC-MS/MS. Procyanidin B2, gallic acid, epicatechin, caffeic acid, quercetin, and chlorogenic acid were measured with higher concentration in these selected unripe fruits. A positive correlation was found between phenolic contents and the antioxidant activities of unripe fruits. Furthermore, chemometric analysis was conducted to validate the results. This study will explore the utilization of these unripe fruits to develop functional and therapeutic foods.

Livestock Identification Using Deep Learning for Traceability.

Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy farm cows using advanced deep-learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment. The video analysis pipeline follows standard human face recognition systems made of four significant steps: (i) face detection, (ii) face cropping, (iii) face encoding, and (iv) face lookup. Three deep learning (DL) models were used within the analysis pipeline: (i) face detector, (ii) landmark predictor, and (iii) face encoder. All DL models were finetuned through transfer learning on a dairy cow dataset collected from a robotic dairy farm located in the Dookie campus at The University of Melbourne, Australia. Results showed that the accuracy across videos from 89 different dairy cows achieved an overall accuracy of 84%. The computer program developed may be deployed on edge devices, and it was tested on NVIDIA Jetson Nano board with a camera stream. Furthermore, it could be integrated into welfare assessment previously developed by our research group.

The Impact of Plant Phytochemicals on the Gut Microbiota of Humans for a Balanced Life.

The gastrointestinal tract of humans is a complex microbial ecosystem known as gut microbiota. The microbiota is involved in several critical physiological processes such as digestion, absorption, and related physiological functions and plays a crucial role in determining the host's health. The habitual consumption of specific dietary components can impact beyond their nutritional benefits, altering gut microbiota diversity and function and could manipulate health. Phytochemicals are non-nutrient biologically active plant components that can modify the composition of gut microflora through selective stimulation of proliferation or inhibition of certain microbial communities in the intestine. Plants secrete these components, and they accumulate in the cell wall and cell sap compartments (body) for their development and survival. These compounds have low bioavailability and long time-retention in the intestine due to their poor absorption, resulting in beneficial impacts on gut microbiota population. Feeding diets containing phytochemicals to humans and animals may offer a path to improve the gut microbiome resulting in improved performance and/or health and wellbeing. This review discusses the effects of phytochemicals on the modulation of the gut microbiota environment and the resultant benefits to humans; however, the effect of phytochemicals on the gut microbiota of animals is also covered, in brief.

A Comparative Study for Nutritional and Phytochemical Profiling of Coffea arabica (C. arabica) from Different Origins and Their Antioxidant Potential and Molecular Docking.

Coffee is the most widely used beverage globally and contains many bioactive compounds, including phenolic compounds, alkaloids, triterpenes, organic acids, amino acids, hormones, and fatty acids. The main objective of this study was the comparative profiling of Australian, Colombian, Ethiopian, and Peruvian C. arabica using LC-ESI-QTOF-MS/MS. In this study, we tentatively identified 136 bioactive metabolites, including five (05) organic acids, six (06) alkaloids, three (03) amino acids (l-phenylalanine, l-tyrosine, and l-pyroglutamic acid), two (02) hormones (melatonin and serotonin), two fatty acids, one (01) furopyrans (goniothalenol), one (01) carotenoid (crocetin), three (03) terpenoids, thirty-eight (38) phenolic acids, forty-one (41) flavonoids, five (05) stilbenes, three (03) lignans and twenty-three (23) other polyphenols in C. arabica. The highest TPC value (17.74 ± 0.32 mg GAE/g) was measured in Colombian coffee while the lowest TPC value (10.24 ± 0.73 mg GAE/g) was in Peruvian coffee. Colombian coffee has a higher antioxidant potential than other studied coffee samples. A total of nineteen phenolic metabolites were mapped through LC-MS/MS. Quinic acid derivatives were quantified in higher concentrations than other metabolites. Furthermore, molecular docking predicted that chlorogenic acid is a main bioactive compound that contributes to anti-Alzheimer and anti-diabetic activities of C. arabica. The obtained results indicate that C. arabica contains a vast number of bioactive compounds which have potential health benefits. Furthermore, research could be conducted to validate the effect of these metabolites on the flavor profile of coffee beverages.

Digital technologies to assess yoghurt quality traits and consumers acceptability.

BACKGROUND: Sensory biometrics provide advantages for consumer tasting by quantifying physiological changes and the emotional response from participants, removing variability associated with self-reported responses. The present study aimed to measure consumers' emotional and physiological responses towards different commercial yoghurts, including dairy and plant-based yoghurts. The physiochemical properties of these products were also measured and linked with consumer responses. RESULTS: Six samples (Control, Coconut, Soy, Berry, Cookies and Drinkable) were evaluated for overall liking by n = 62 consumers using a nine-point hedonic scale. Videos from participants were recorded using the Bio-Sensory application during tasting to assess emotions and heart rate. Physicochemical parameters Brix, pH, density, color (L, a and b), firmness and near-infrared (NIR) spectroscopy were also measured. Principal component analysis and a correlation matrix were used to assess relationships between the measured parameters. Heart rate was positively related to firmness, yaw head movement and overall liking, which were further associated with the Cookies sample. Two machine learning regression models were developed using (i) NIR absorbance values as inputs to predict the physicochemical parameters (Model 1) and (ii) the outputs from Model 1 as inputs to predict consumers overall liking (Model 2). Both models presented very high accuracy (Model 1: R = 0.98; Model 2: R = 0.99). CONCLUSION: The presented methods were shown to be highly accurate and reliable with respect to their potential use by the industry to assess yoghurt quality traits and acceptability. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Impacts of heat stress on immune responses and oxidative stress in farm animals and nutritional strategies for amelioration.

Heat stress is one of the greatest challenges for the global livestock industries as increased environmental temperature and humidity compromises animal production during summer leading to devastating economic consequences. Over the last 30 years, significant developments have been achieved in cooling and provision of shade and shelter to mitigate heat stress reducing some of the losses associated with heat stress in farm animals. However, the recent increase in the incidence of heat waves which are also becoming more severe and lasting longer, due to climate change, further accentuates the problem of heat stress. Economic losses associated with heat stress are both direct due to loss in production and animal life, and indirect due to poorer quality products as a result of poor animal health and welfare. Animal health is affected due to impaired immune responses and increased reactive oxygen species production and/or deficiency of antioxidants during heat stress leading to an imbalance between oxidant and antioxidants and resultant oxidative stress. Research over the last 20 years has achieved partial success in understanding the intricacies of heat stress impacts on oxidative stress and immune responses and developing interventions to ameliorate impacts of heat stress, improving immune responses and farm animal health. This paper reviews the body of knowledge on heat stress impacts on immune response in farm animals. The impacts of heat stress on both cell-mediated and humoral immune responses have been discussed identifying the shift in immune response from cell-mediated towards humoral response, thereby weakening the immune status of the animal. Both species and breed differences have been identified as influencing how heat stress impacts the immune status of farm animals. In addition, crosstalk signaling between the immune system and oxidative stress has been considered and the role of antioxidants as potential nutritional strategies to mitigate heat stress has been discussed.

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence.

New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow's heart rate, respiration rate, and abrupt movements captured using RGB cameras and machine learning modelling to predict eye temperature, milk production and quality are presented. RGB and infrared thermal videos (IRTV) were acquired from cows using a robotic milking facility. Results from 102 different cows with replicates (n = 150) showed that an artificial neural network (ANN) model using only inputs from RGB cameras presented high accuracy (R = 0.96) in predicting eye temperature (°C), using IRTV as ground truth, daily milk productivity (kg-milk-day-1), cow milk productivity (kg-milk-cow-1), milk fat (%) and milk protein (%) with no signs of overfitting. The ANN model developed was deployed using an independent 132 cow samples obtained on different days, which also rendered high accuracy and was similar to the model development (R = 0.93). This model can be easily applied using affordable RGB camera systems to obtain all the proposed targets, including eye temperature, which can also be used to model animal welfare and biotic/abiotic stress. Furthermore, these models can be readily deployed in conventional dairy farms.

Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis.

New and emerging non-invasive digital tools, such as eye-tracking, facial expression and physiological biometrics, have been implemented to extract more objective sensory responses by panelists from packaging and, specifically, labels. However, integrating these technologies from different company providers and software for data acquisition and analysis makes their practical application difficult for research and the industry. This study proposed a prototype integration between eye tracking and emotional biometrics using the BioSensory computer application for three sample labels: Stevia, Potato chips, and Spaghetti. Multivariate data analyses are presented, showing the integrative analysis approach of the proposed prototype system. Further studies can be conducted with this system and integrating other biometrics available, such as physiological response with heart rate, blood, pressure, and temperature changes analyzed while focusing on different label components or packaging features. By maximizing data extraction from various components of packaging and labels, smart predictive systems can also be implemented, such as machine learning to assess liking and other parameters of interest from the whole package and specific components.

Maternal Heat Stress Alters Expression of Genes Associated with Nutrient Transport Activity and Metabolism in Female Placentae from Mid-Gestating Pigs.

Placental insufficiency is a known consequence of maternal heat stress during gestation in farm animals. The molecular regulation of placentae during the stress response is little known in pigs. This study aims to identify differential gene expression in pig placentae caused by maternal heat exposure during early to mid-gestation. RNA sequencing (RNA-seq) was performed on female placental samples from pregnant pigs exposed to thermoneutral control (CON; constant 20 °C; n = 5) or cyclic heat stress (HS; cyclic 28 to 33 °C; n = 5) conditions between d40 and d60 of gestation. On d60 of gestation, placental efficiency (fetal/placental weight) was decreased (p = 0.023) by maternal HS. A total of 169 genes were differentially expressed (FDR ≤ 0.1) between CON and HS placentae of female fetuses, of which 35 genes were upregulated and 134 genes were downregulated by maternal HS. The current data revealed transport activity (FDR = 0.027), glycoprotein biosynthetic process (FDR = 0.044), and carbohydrate metabolic process (FDR = 0.049) among the terms enriched by the downregulated genes (HS vs. CON). In addition, solute carrier (SLC)-mediated transmembrane transport (FDR = 0.008) and glycosaminoglycan biosynthesis (FDR = 0.027), which modulates placental stroma synthesis, were identified among the pathways enriched by the downregulated genes. These findings provide evidence that heat-stress induced placental inefficiency may be underpinned by altered expression of genes associated with placental nutrient transport capacity and metabolism. A further understanding of the molecular mechanism contributes to the identification of placental gene signatures of summer infertility in pigs.

LC-ESI-QTOF-MS/MS Characterization of Seaweed Phenolics and Their Antioxidant Potential.

Seaweed is an important food widely consumed in Asian countries. Seaweed has a diverse array of bioactive compounds, including dietary fiber, carbohydrate, protein, fatty acid, minerals and polyphenols, which contribute to the health benefits and commercial value of seaweed. Nevertheless, detailed information on polyphenol content in seaweeds is still limited. Therefore, the present work aimed to investigate the phenolic compounds present in eight seaweeds [Chlorophyta (green), Ulva sp., Caulerpa sp. and Codium sp.; Rhodophyta (red), Dasya sp., Grateloupia sp. and Centroceras sp.; Ochrophyta (brown), Ecklonia sp., Sargassum sp.], using liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF-MS/MS). The total phenolic content (TPC), total flavonoid content (TFC) and total tannin content (TTC) were determined. The antioxidant potential of seaweed was assessed using a 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay, a 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) free radical scavenging assay and a ferric reducing antioxidant power (FRAP) assay. Brown seaweed species showed the highest total polyphenol content, which correlated with the highest antioxidant potential. The LC-ESI-QTOF-MS/MS tentatively identified a total of 54 phenolic compounds present in the eight seaweeds. The largest number of phenolic compounds were present in Centroceras sp. followed by Ecklonia sp. and Caulerpa sp. Using high-performance liquid chromatography-photodiode array (HPLC-PDA) quantification, the most abundant phenolic compound was p-hydroxybenzoic acid, present in Ulva sp. at 846.083 ± 0.02 µg/g fresh weight. The results obtained indicate the importance of seaweed as a promising source of polyphenols with antioxidant properties, consistent with the health potential of seaweed in food, pharmaceutical and nutraceutical applications.

Impacts of heat stress on meat quality and strategies for amelioration: a review.

During the summer, high ambient temperature and humidity cause economic loss to the global livestock industry via reduced livestock productivity and increased mortality. The problem of heat stress (HS) is likely to be exacerbated by global warming and climate change. Recent research has shown that HS not only leads to physiological and metabolic perturbations in live animals but can also affect carcass and meat quality characteristics plausibly by altering the rate and extent of postmortem muscle glycolysis and resultant pH. However, these impacts of HS are not consistent across species. Higher incidence of pale soft and exudative (PSE) meat has been reported in poultry. On the contrary, higher incidence of high ultimate pH and dark firm and dry (DFD) meat or no impacts of HS have been reported in sheep and cattle. With the limited data on HS impacts on meat quality of ruminants, it is difficult to explain the exact mechanisms driving these variable impacts. However, it is hypothesized that the severity and duration of HS may lead to variable impacts due to lack of opportunity to adapdate to acute heat exposure. Longer HS exposure may allow ruminants to adapdate to heat and may not record any negative impacts on meat quality. This paper reviews the recent research on impacts of HS on meat quality characteristics and identify the key areas of further research required to better understand these negative impacts to develop strategies for amelioration. In addition, some mitigation strategies of HS have also been discussed which include both managemental and nutritional interventions.

Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras.

Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting.

Artificial Intelligence Applied to a Robotic Dairy Farm to Model Milk Productivity and Quality based on Cow Data and Daily Environmental Parameters.

Increased global temperatures and climatic anomalies, such as heatwaves, as a product of climate change, are impacting the heat stress levels of farm animals. These impacts could have detrimental effects on the milk quality and productivity of dairy cows. This research used four years of data from a robotic dairy farm from 36 cows with similar heat tolerance (Model 1), and all 312 cows from the farm (Model 2). These data consisted of programmed concentrate feed and weight combined with weather parameters to develop supervised machine learning fitting models to predict milk yield, fat and protein content, and actual cow concentrate feed intake. Results showed highly accurate models, which were developed for cows with a similar genetic heat tolerance (Model 1: n = 116, 456; R = 0.87; slope = 0.76) and for all cows (Model 2: n = 665, 836; R = 0.86; slope = 0.74). Furthermore, an artificial intelligence (AI) system was proposed to increase or maintain a targeted level of milk quality by reducing heat stress that could be applied to a conventional dairy farm with minimal technology addition.

Consumer rejection threshold, acceptability rates, physicochemical properties, and shelf-life of strawberry-flavored yogurts with reductions of sugar.

BACKGROUND: There is an increasing demand for reduced-sugar products due to the worldwide prevalence of obesity, diabetes, and cardiovascular diseases. The aim of this study was to evaluate the effects of sugar (sucrose) reductions on the acceptability, preference, and quality of strawberry-flavored yogurts. A consumer rejection threshold test and an acceptability test (N = 53) were conducted using six yogurt samples with decreasing concentrations of sugar (12-5/100 g). Additional physicochemical tests (pH, °Brix, water-holding-capacity, viscosity, and color) were conducted to examine the quality and shelf-life of strawberry-flavored yogurts with reductions of sucrose during 28 days of storage at 4 °C. RESULTS: Reduction of sucrose affected the acceptability and physicochemical characteristics of yogurts. The consumer rejection threshold showed that sucrose in strawberry-flavored yogurts could be reduced to 5.25/100 g from an initial concentration of 12/100 g without affecting the preferences of consumers. The 71%-sucrose (8.50/100 g of yogurt) was perceived as the most liked (6.27 using a nine-point hedonic scale) and the most preferred (rank sum = 127.50) yogurt sample. For the physicochemical properties of yogurts, the viscosity (3263-5473 cP) decreased, and the color lightness (80.98-85.44) increased during 28 days of storage at 4 °C. CONCLUSION: Physicochemical properties and preferences were affected by the reduction of sugar. The consumer rejection threshold analysis showed that sucrose can be reduced to less than half of the initial concentration. These findings are useful to understand consumers' acceptability and shelf-life of yogurts with reduced-sugar formulations in the developing of new products. © 2020 Society of Chemical Industry.

LC-ESI-QTOF/MS characterization of bioactive compounds from black spices and their potential antioxidant activities.

Black pepper (Piper nigrum L.), black cumin (Nigella sativa L.) and black cardamom (Amomum subulatum) are considered as important spices, seasoning and folk medicines. They have a diverse range of bioactive compounds, especially for polyphenolic compounds. These polyphenolic compounds contribute to the putative health benefits of these black spices. The purpose of this study was to identify, characterize and quantify the phenolic profile of these black spices using LC-ESI-QTOF/MS and HPLC-PDA and to access their antioxidant potential. The LC-ESI-QTOF/MS analysis led to the identification of 138 phenolic compounds in three black spices. In HPLC-PDA, the p-hydroxybenzoic acid was the most predominant phenolic acid in black pepper and black cumin while diosmin was the most abundant flavonoid in black cardamom (> 20 mg/g). Furthermore, black spices were systematically measured for their TPC, TFC and TTC followed by measurement of their antioxidant activities using DPPH, FRAP and ABTS assays. Black pepper showed the highest TPC, TFC, TTC, DPPH and ABTS activities as compared to other black spices while black cardamom exhibited the highest FRAP activity. The obtained results highlight the importance of these black spices as promising sources of phenolic compounds and they could be potentially utilized in food, feed and nutraceutical industries.

Effects of L-citrulline supplementation on heat stress physiology, lactation performance and subsequent reproductive performance of sows in summer.

Lactating sows are susceptible to heat stress (HS). Part of the thermoregulatory response to HS is to increase peripheral blood flow, which is mediated in part by the vasodilator, nitric oxide (NO). Therefore, the aim of this experiment was to determine the effect of supplementation of L-citrulline, a NO precursor, on symptoms of HS, lactation performance and subsequent reproductive performance of sows in summer. A total of 221 summer farrowing mixed parity sows were fed either a control diet or supplemented with 1% L-citrulline upon entry to the farrowing house (6 ± 1.8 days for mean ± standard deviation [SD] before farrowing) until weaning (26 ± 1.5 days). The average daily minimum and maximum temperature in the farrowing house was 21.0 ± 1.88 and 29.2 ± 3.82°C (mean ± SD). Rectal temperature, respiration rate, and plasma and urinary nitrite and nitrate (NOx) of sows were measured on the 19th day post-farrowing. Supplemental L-citrulline in the diet did not affect the number of piglets born alive, feed intake of sows, body weight or backfat thickness of sows at weaning, or litter weight gain. L-citrulline tended to reduce piglet pre-weaning mortality rate from 18.6% to 15.6% (p = 0.058). L-citrulline reduced the respiration rate of sows compared to the control diet at 17:00 hr (Time × Diet, p < 0.001); however, rectal temperature was not affected. L-citrulline tended to increase urinary NOx concentrations (127 vs. 224 µM, p = 0.057) but not plasma NOx concentrations. L-citrulline did not affect farrowing rate or number of piglets born alive in the subsequent parity. In conclusion, L-citrulline supplementation reduced respiration rate of lactating sows and reduced piglet pre-weaning mortality rate in summer. Whether the effects were due to a NO-dependent mechanism requires further validation.

Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists.

In sensory evaluation, there have been many attempts to obtain responses from the autonomic nervous system (ANS) by analyzing heart rate, body temperature, and facial expressions. However, the methods involved tend to be intrusive, which interfere with the consumers' responses as they are more aware of the measurements. Furthermore, the existing methods to measure different ANS responses are not synchronized among them as they are measured independently. This paper discusses the development of an integrated camera system paired with an Android PC application to assess sensory evaluation and biometric responses simultaneously in the Cloud, such as heart rate, blood pressure, facial expressions, and skin-temperature changes using video and thermal images acquired by the integrated system and analyzed through computer vision algorithms written in Matlab®, and FaceReaderTM. All results can be analyzed through customized codes for multivariate data analysis, based on principal component analysis and cluster analysis. Data collected can be also used for machine-learning modeling based on biometrics as inputs and self-reported data as targets. Based on previous studies using this integrated camera and analysis system, it has shown to be a reliable, accurate, and convenient technique to complement the traditional sensory analysis of both food and nonfood products to obtain more information from consumers and/or trained panelists.