Assessing the influence of spontaneous fermentation on consumer emotional responses to roasted arabica coffee in a biometric approach.

Beyond sensory quality, food-evoked emotions play a crucial role in consumers acceptance and willingness to try, which are essential for product development. The link between fermented coffee sensory characteristics and elicited emotional responses from consumers is underexplored. This study aimed to evaluate consumers' acceptability of spontaneously fermented and unfermented roasted coffee through self-reported sensory evaluation and biometrics assessment. Self-reported liking in 15-cm non-structured scale, multiple choice of negative, neutral, and positive emojis, and subconscious emotional responses from 85 regular coffee consumers were analysed. Their relationship with the pattern of volatile aromatic compounds were also investigated. Fermented (F) and unfermented (UF) coffee beans with light- (L), dark- (D), and commercial dark (C) roasting levels were brewed and evaluated along with gas chromatography-mass spectrometry measurement. Multivariate data analysis was conducted to explore the inner relationships among volatile compounds, self-reported liking, and biometrics. Unfermented-dark roasted coffee (UFD) had highest overall consumer liking response ± standard error (8.68 ± 0.40), followed by the fermented-dark roasted (FD) at 7.73 ± 0.43 with no significant differences (p > 0.05). Fermented light-roasted coffee was associated with lower liking scores and negative emotional responses. In contrast, dark roasted coffee, which was linked to positive emojis and emotional responses, exhibited less detected peak area of volatile compounds contributing fruity and vegetative aromas, such as benzaldehyde, furfuryl acetate, 2-acetyl-1-methyl pyrrole, and isovaleric acid, potentially as negative drivers of consumer liking. Findings from this study could guide coffee manufacturers in developing specialty coffee if spontaneous fermentation is offered.

Consumer eating quality and physicochemical traits of pork Longissimus and Semimembranosus differed between genetic lines.

Pork eating quality is affected by various factors. In this study, Longissimus thoracis et lumborum (LTL) and Semimembranosus (SM) muscles from seven genetic lines (PM-LR - Pure maternal, Landrace-type; PM-LW - Pure maternal, Large White-type; PM-D - Pure maternal, Duroc-type; PT-D - Pure terminal, Duroc-type; PT-LW - Pure terminal, Large White-type; PT-LR - Pure Terminal, Landrace-type; Comp-P × LW × D - Composite Terminal - Pietran × Large white × Duroc) were analyzed for pH, intramuscular fat (IMF) content, and collagen content and solubility. A consumer sensory test using check-all-that-apply (CATA) and biometric approaches was also conducted. The results showed that the IMF content of line PM-D was the highest (P = 0.004), while line PT-LW received the highest score in tenderness, liking of flavor, purchase intent, and quality grading (P < 0.05). Line PM-LR and PT-LR showed the lowest IMF content and were least preferred by consumers. Compared to LTL, SM showed higher pH, collagen solubility, and sensory scores in tenderness, juiciness, liking of flavor, and overall liking (P < 0.05). Different muscles and lines were associated with different CATA terms but not with differences in consumer emotional responses. pH positively influenced tenderness, juiciness, and overall liking (P < 0.05), but IMF and collagen had little effect. The flavor was the most important sensory attribute contributing to overall liking, followed by tenderness. Genetic line and muscle affected pork chemical properties and eating quality. The findings are important for the Australian pork industry to improve the eating quality of their products.

Consumer insights into the at-home liking of commercial beers: Integrating nonvolatile and volatile flavor chemometrics.

Consumer acceptability of beers is influenced by product formulation and processing conditions, which impart unique sensory profiles. This study used multivariate techniques to evaluate at-home consumer sensory acceptability of six commercial beers considering their style, fermentation type, and chemical composition. Samples included top-fermented beers (American India Pale Ale and Stout) and bottom-fermented beers (Pilsner, zero-alcohol Pilsner, Vienna Lager, and Munich Dunkel). Beer consumers (n = 50) conducted sensory hedonic, check-all-that-apply (CATA) and just-about-right (JAR) tests. Chemometric variables included iso-alpha-acids, hordenine, and volatile aromatic compounds, quantified by chromatographic methods, whereas bitterness units (IBU) were determined spectrophotometrically. Lager beers had higher acceptability than top-fermented beer (p < .05) for all attributes. Light-colored beers and medium-height foams had the highest liking scores for visual sensory attributes. Higher concentrations of bitter-tasting molecules, hordenine, and acidity decreased the liking scores of top-fermented (Ale) beers, as a sensory penalty analysis suggested. In contrast, the most favored beers (Pilsners and Munich Dunkel) contained higher fusel alcohol esters linked to fruity aromatic notes. Although a low conversion rate of fatty acids into fruity esters was noted in nonalcoholic Pilsner, its overall liking score was not statistically different from the alcoholic version. However, consumers perceived the nonalcoholic Pilsner as less bitter than its alcoholic counterpart even when IBUs were nonsignificantly different. This study emphasized the significance of understanding beer chemometrics to comprehend consumer acceptability, highlighting the crucial role of bitter molecules. Hence, hordenine, acidity, and volatile contents provided additional and valuable insights into consumer preferences.

Evaluation of spontaneous fermentation impact on the physicochemical properties and sensory profile of green and roasted arabica coffee by digital technologies.

There is a growing demand for specialty coffee with more pleasant and uniform sensory perception. Wet fermentation could modulate and confer additional aroma notes to final roasted coffee brew. This study aimed to assess differences in volatile compounds and the intensities of sensory descriptors between unfermented and spontaneously fermented coffee using digital technologies. Fermented (F) and unfermented (UF) coffee samples, harvested from two Australia local farms Mountain Top Estate (T) and Kahawa Estate (K), with four roasting levels (green, light-, medium-, and dark-) were analysed using near-infrared spectrometry (NIR), and a low-cost electronic nose (e-nose) along with some ground truth measurements such as headspace/gas chromatography-mass spectrometry (HS-SPME-GC-MS), and quantitative descriptive analysis (QDA ®). Regression machine learning (ML) modelling based on artificial neural networks (ANN) was conducted to predict volatile aromatic compounds and intensity of sensory descriptors using NIR and e-nose data as inputs. Green fermented coffee had significant perception of hay aroma and flavor. Roasted fermented coffee had higher intensities of coffee liquid color, crema height and color, aftertaste, aroma and flavor of dark chocolate and roasted, and butter flavor (p < 0.05). According to GC-MS detection, volatile aromatic compounds, including methylpyrazine, 2-ethyl-5-methylpyrazine, and 2-ethyl-6-methylpyrazine, were observed to discriminate fermented and unfermented roasted coffee. The four ML models developed using the NIR absorbance values and e-nose measurements as inputs were highly accurate in predicting (i) the peak area of volatile aromatic compounds (Model 1, R = 0.98; Model 3, R = 0.87) and (ii) intensities of sensory descriptors (Model 2 and Model 4; R = 0.91), respectively. The proposed efficient, reliable, and affordable method may potentially be used in the coffee industry and smallholders in the differentiation and development of specialty coffee, as well as in process monitoring and sensory quality assurance.

Assessment of changes in sensory perception, biometrics and emotional response for spaceexploration by simulating microgravity positions.

Long-term space exploration endeavors, encompassing journeys from the Earth to the Moon by 2030 and subsequent voyages from the Moon to Mars by 2040, necessitate the utilization of plant-based materials not solely for sustenance and refreshments but also the production of pharmaceuticals and repair compounds, such as plastics, among others. Nevertheless, the vital aspects of research in this domain pertain to the nutritional value and sensory perception associated with plant-based food. Prior investigations have shown altered sensory perception in space, manifested as diminished olfactory sensations and heightened taste perception (saltiness and sweetness). Nonetheless, studies concerning changes in aroma, basic tastes, and mouthfeel have been limited due to the logistical challenges associated with conducting experiments in the unique environment of space. To address this limitation, the present study employed sensory trials and biometrics from video using simulated microgravity chairs to simulate alterations in sensory perception akin to those encountered in space conditions. The findings of this study align with previous reports of changes in aroma and taste perception and contribute to the understanding of changes in the mouthfeel, heart rate, blood pressure, and emotional response that could be experienced in space environments. These experimental endeavors are critical to facilitate the advancement and development of novel plants and food materials tailored to the requirements of long-term space exploration.

Novel insights into the mechanism(s) of silicon-induced drought stress tolerance in lentil plants revealed by RNA sequencing analysis.

BACKGROUND: Lentil is an essential cool-season food legume that offers several benefits in human nutrition and cropping systems. Drought stress is the major environmental constraint affecting lentil plants' growth and productivity by altering various morphological, physiological, and biochemical traits. Our previous research provided physiological and biochemical evidence showing the role of silicon (Si) in alleviating drought stress in lentil plants, while the molecular mechanisms are still unidentified. Understanding the molecular mechanisms of Si-mediated drought stress tolerance can provide fundamental information to enhance our knowledge of essential gene functions and pathways modulated by Si during drought stress in plants. Thus, the present study compared the transcriptomic characteristics of two lentil genotypes (drought tolerant-ILL6002; drought sensitive-ILL7537) under drought stress and investigated the gene expression in response to Si supplementation using high-throughput RNA sequencing. RESULTS: This study identified 7164 and 5576 differentially expressed genes (DEGs) from drought-stressed lentil genotypes (ILL 6002 and ILL 7537, respectively), with Si treatment. RNA sequencing results showed that Si supplementation could alter the expression of genes related to photosynthesis, osmoprotection, antioxidant systems and signal transduction in both genotypes under drought stress. Furthermore, these DEGs from both genotypes were found to be associated with the metabolism of carbohydrates, lipids and proteins. The identified DEGs were also linked to cell wall biosynthesis and vasculature development. Results suggested that Si modulated the dynamics of biosynthesis of alkaloids and flavonoids and their metabolism in drought-stressed lentil genotypes. Drought-recovery-related DEGs identified from both genotypes validated the role of Si as a drought stress alleviator. This study identified different possible defense-related responses mediated by Si in response to drought stress in lentil plants including cellular redox homeostasis by reactive oxygen species (ROS), cell wall reinforcement by the deposition of cellulose, lignin, xyloglucan, chitin and xylan, secondary metabolites production, osmotic adjustment and stomatal closure. CONCLUSION: Overall, the results suggested that a coordinated interplay between various metabolic pathways is required for Si to induce drought tolerance. This study identified potential genes and different defence mechanisms involved in Si-induced drought stress tolerance in lentil plants. Si supplementation altered various metabolic functions like photosynthesis, antioxidant defence system, osmotic balance, hormonal biosynthesis, signalling, amino acid biosynthesis and metabolism of carbohydrates and lipids under drought stress. These novel findings validated the role of Si in drought stress mitigation and have also provided an opportunity to enhance our understanding at the genomic level of Si's role in alleviating drought stress in plants.

Novel Contactless Sensors for Food, Beverage and Packaging Evaluation.

The use of traditional methods to evaluate food, beverages, and packaging tends to be time-consuming, labour-intensive, and usually involves high costs due to the need for expensive equipment, regular refill of consumables, skilled personnel and, in the case of sensory evaluation, incentives or payments involved for participants recruitment and/or panelists training and participation [...].

Review of technology advances to assess rice quality traits and consumer perception.

The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.

Infrared Thermal Imaging and Morpho-Physiological Indices Used for Wheat Genotypes Screening under Drought and Heat Stress.

Bread wheat, one of the largest broadacre crops, often experiences various environmental stresses during critical growth stages. Terminal drought and heat stress are the primary causes of wheat yield reduction worldwide. This study aimed to determine the drought and heat stress tolerance level of a group of 46 diverse wheat genotypes procured from the Australian Grains Gene Bank, Horsham, VIC Australia. Two separate drought stress (DS) and heat stress (HS) pot experiments were conducted in separate growth chambers. Ten days after complete anthesis, drought (40 ± 3% field capacity for 14 days) and heat stress (36/22 °C for three consecutive days) were induced. A significant genotype × environment interaction was observed and explained by various morpho-physiological traits, including rapid, non-destructive infrared thermal imaging for computational water stress indices. Except for a spike length in DS and harvest index in HS, the analysis of variance showed significant differences for all the recorded traits. Results showed grains per spike, grains weight per spike, spike fertility, delayed flag leaf senescence, and cooler canopy temperature were positively associated with grain yield under DS and HS. The flag leaf senescence and chlorophyll fluorescence were used to measure each genotype's stay-green phenotype and photosystem II activity after DS and HS. This study identified the top ten best and five lowest-performing genotypes from drought and heat stress experiments based on their overall performance. Results suggest that if heat or drought adaptive traits are brought together in a single genotype, grain yield can be improved further, particularly in a rainfed cropping environment.

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.

Early Detection of Fusarium oxysporum Infection in Processing Tomatoes (Solanum lycopersicum) and Pathogen-Soil Interactions Using a Low-Cost Portable Electronic Nose and Machine Learning Modeling.

The early detection of pathogen infections in plants has become an important aspect of integrated disease management. Although previous research demonstrated the idea of applying digital technologies to monitor and predict plant health status, there is no effective system for detecting pathogen infection before symptomatology appears. This paper presents the use of a low-cost and portable electronic nose coupled with machine learning (ML) models for early disease detection. Several artificial neural network models were developed to predict plant physiological data and classify processing tomato plants and soil samples according to different levels of pathogen inoculum by using e-nose outputs as inputs, plant physiological data, and the level of infection as targets. Results showed that the pattern recognition models based on different infection levels had an overall accuracy of 94.4-96.8% for tomato plants and between 94.81% and 96.22% for soil samples. For the prediction of plant physiological parameters (photosynthesis, stomatal conductance, and transpiration) using regression models or tomato plants, the overall correlation coefficient was 0.97-0.99, with very significant slope values in the range 0.97-1. The performance of all models shows no signs of under or overfitting. It is hence proven accurate and valid to use the electronic nose coupled with ML modeling for effective early disease detection of processing tomatoes and could also be further implemented to monitor other abiotic and biotic stressors.

Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning.

Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice's weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94-0.98) and non-invasive measurement through the packaging (NIR; R = 0.95-0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.

Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture.

When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies [...].

The role of silicon in regulating physiological and biochemical mechanisms of contrasting bread wheat cultivars under terminal drought and heat stress environments.

The individual and cumulative effects of drought stress (DS) and heat stress (HS) are the primary cause of grain yield (GY) reduction in a rainfed agricultural system. Crop failures due to DS and HS are predicted to increase in the coming years due to increasingly severe weather events. Plant available silicon (Si, H4SiO4) has been widely reported for its beneficial effects on plant development, productivity, and attenuating physiological and biochemical impairments caused by various abiotic stresses. The current study investigated the impact of pre-sowing Si treatment on six contrasting wheat cultivars (four drought and heat stress-tolerant and two drought and heat stress-susceptible) under individual and combined effects of drought and heat stress at an early grain-filling stage. DS, HS, and drought-heat combined stress (DHS) significantly (p < 0.05) altered morpho-physiological and biochemical attributes in susceptible and tolerant wheat cultivars. However, results showed that Si treatment significantly improved various stress-affected morpho-physiological and biochemical traits, including GY (>40%) and yield components. Si treatment significantly (p < 0.001) increased the reactive oxygen species (ROS) scavenging antioxidant activities at the cellular level, which is linked with higher abiotic stress tolerance in wheat. With Si treatment, osmolytes concentration increased significantly by >50% in tolerant and susceptible wheat cultivars. Similarly, computational water stress indices (canopy temperature, crop water stress index, and canopy temperature depression) also improved with Si treatment under DS, HS, and DHS in susceptible and tolerant wheat cultivars. The study concludes that Si treatment has the potential to mitigate the detrimental effects of individual and combined stress of DS, HS, and DHS at an early grain-filling stage in susceptible and tolerant wheat cultivars in a controlled environment. These findings also provide a foundation for future research to investigate Si-induced tolerance mechanisms in susceptible and tolerant wheat cultivars at the molecular level.

The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence.

Livestock welfare assessment helps monitor animal health status to maintain productivity, identify injuries and stress, and avoid deterioration. It has also become an important marketing strategy since it increases consumer pressure for a more humane transformation in animal treatment. Common visual welfare practices by professionals and veterinarians may be subjective and cost-prohibitive, requiring trained personnel. Recent advances in remote sensing, computer vision, and artificial intelligence (AI) have helped developing new and emerging technologies for livestock biometrics to extract key physiological parameters associated with animal welfare. This review discusses the livestock farming digital transformation by describing (i) biometric techniques for health and welfare assessment, (ii) livestock identification for traceability and (iii) machine and deep learning application in livestock to address complex problems. This review also includes a critical assessment of these topics and research done so far, proposing future steps for the deployment of AI models in commercial farms. Most studies focused on model development without applications or deployment for the industry. Furthermore, reported biometric methods, accuracy, and machine learning approaches presented some inconsistencies that hinder validation. Therefore, it is required to develop more efficient, non-contact and reliable methods based on AI to assess livestock health, welfare, and productivity.

Remote sensory assessment of beer quality based on visual perception of foamability and biometrics compared to standard emotional responses from affective images.

The social isolation settings derived from the COVID-19 pandemic affected the standard sensory evaluation techniques used in the food and beverage industry. This situation forced companies and researchers to assess other options to continue conducting these tests in remote contactless locations. This study aimed to evaluate two sets of samples (i) six images from Geneva affective picture database (GAPED) and (ii) six videos of beer pouring using traditional self-reported sensory data and emotional response from consumers biometrics. Specifically, four research questions (RQ) arouse from this study: RQ1: are there significant differences between GAPED images and beers in unconscious and self-reported responses from consumers?, RQ2: are there any correlations between subconscious and self-reported responses from consumers when assessing beer?, RQ3: can consumers differentiate positive, neutral and negative images based on subconscious and self-reported responses?, RQ4: are there any relationships between subconscious and self-reported responses when assessing GAPED images and beers, and how are samples associated with variables? A total of 113 Mexican beer consumers participated in the virtual sensory session using an online videoconference software to record videos of participants during the session. Results showed there were significant differences (p < 0.05) between samples, especially for self-reported responses (RQ1), and several correlations between variables, such as positive correlations between the perceived quality of beers and happy emoji (r = 0.84), and negative correlation with confused emoji (r = -0.97; RQ2). Besides, using the proposed methods, consumers were able to correctly differentiate through elicited emotions the positive, neutral and negative GAPED images (RQ3). Regarding RQ4, several relationships were found between variables in both GAPED images and beers; however, it was found that different emotions were elicited depending of the stimuli used. The proposed method showed to be a reliable and practical option to conduct visual and potentially tasting sensory tests in isolation and recruit participants from different countries without travelling to collect their responses.

Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies.

Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.

Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment.

In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries [...].

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.