scholarly journals Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3618
Author(s):  
Sigfredo Fuentes ◽  
Damir D. Torrico ◽  
Eden Tongson ◽  
Claudia Gonzalez Viejo
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Pinot Noir ◽  
Sensory Profile ◽  
Quality Traits ◽  
Wine Quality ◽  
Complex Interactions ◽  
Chemical Fingerprinting ◽  
Artificial Neural Network Ann ◽  
Sensory Profiles

Important wine quality traits such as sensory profile and color are the product of complex interactions between the soil, grapevine, the environment, management, and winemaking practices. Artificial intelligence (AI) and specifically machine learning (ML) could offer powerful tools to assess these complex interactions and their patterns through seasons to predict quality traits to winegrowers close to harvest and before winemaking. This study considered nine vintages (2008–2016) using near-infrared spectroscopy (NIR) of wines and corresponding weather and management information as inputs for artificial neural network (ANN) modeling of sensory profiles (Models 1 and 2 respectively). Furthermore, weather and management data were used as inputs to predict the color of wines (Model 3). Results showed high accuracy in the prediction of sensory profiles of vertical wine vintages using NIR (Model 1; R = 0.92; slope = 0.85), while better models were obtained using weather/management data for the prediction of sensory profiles (Model 2; R = 0.98; slope = 0.93) and wine color (Model 3; R = 0.99; slope = 0.98). For all models, there was no indication of overfitting as per ANN specific tests. These models may be used as powerful tools to winegrowers and winemakers close to harvest and before the winemaking process to maintain a determined wine style with high quality and acceptability by consumers.

scholarly journals A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting

Fermentation ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 73 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Sigfredo Fuentes
Keyword(s):  
Near Infrared ◽  
Supervised Machine Learning ◽  
Digital Tools ◽  
Quality Traits ◽  
Model Quality ◽  
Chemical Fingerprinting ◽  
Brewing Process ◽  
Virtual Simulations ◽  
Assess Quality ◽  
Sensory Profiles

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.

scholarly journals Beer Aroma and Quality Traits Assessment Using Artificial Intelligence

Fermentation ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 56 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Near Infrared ◽  
Consumer Preferences ◽  
Low Cost ◽  
Physicochemical Characteristics ◽  
Decision Making Process ◽  
Quality Traits ◽  
Chemical Fingerprinting ◽  
Machine Learning Models

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.

scholarly journals Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7312
Author(s):  
Sigfredo Fuentes ◽  
Claudia Gonzalez Viejo ◽  
Chelsea Hall ◽  
Yidan Tang ◽  
Eden Tongson
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Cell Death ◽  
Near Infrared ◽  
Quality Traits ◽  
Canopy Architecture ◽  
Wine Quality ◽  
Cell Vitality ◽  
Berry Quality ◽  
And Performance

Berry cell death assessment can become one of the most objective parameters to assess important berry quality traits, such as aroma profiles that can be passed to the wine in the winemaking process. At the moment, the only practical tool to assess berry cell death in the field is using portable near-infrared spectroscopy (NIR) and machine learning (ML) models. This research tested the NIR and ML approach and developed supervised regression ML models using Shiraz and Chardonnay berries and wines from a vineyard located in Yarra Valley, Victoria, Australia. An ML model was developed using NIR measurements from intact berries as inputs to estimate berry cell death (BCD), living tissue (LT) (Model 1). Furthermore, canopy architecture parameters obtained from cover photography of grapevine canopies and computer vision analysis were also tested as inputs to develop ML models to assess BCD and LT (Model 2) and the intensity of sensory descriptors based on visual and aroma profiles of wines for Chardonnay (Model 3) and Shiraz (Model 4). The results showed high accuracy and performance of models developed based on correlation coefficient (R) and slope (b) (M1: R = 0.87; b = 0.82; M2: R = 0.98; b = 0.93; M3: R = 0.99; b = 0.99; M4: R = 0.99; b = 1.00). Models developed based on canopy architecture, and computer vision can be used to automatically estimate the vigor and berry and wine quality traits using proximal remote sensing and with visible cameras as the payload of unmanned aerial vehicles (UAV).

scholarly journals Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5099
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Colleen Szeto ◽  
Kerry L. Wilkinson ◽  
Damir D. Torrico ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Detection System ◽  
Nir Spectroscopy ◽  
Machine Learning Algorithms ◽  
Anthocyanin Content ◽  
Sample Collection ◽  
Neural Network Models ◽  
Field Detection ◽  
Chemical Fingerprinting

Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.

scholarly journals Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence

Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 117
Author(s):  
Claudia Gonzalez Viejo ◽  
Sigfredo Fuentes ◽  
Carmen Hernandez-Brenes
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Electronic Nose ◽  
Near Infrared ◽  
Low Cost ◽  
Regularization Algorithm ◽  
Quality Product ◽  
Brewing Process ◽  
Artificial Neural Network Ann

Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling. For these purposes, a commercial larger beer was used as a base prototype, which was spiked with 18 common beer faults plus the control aroma. The 19 aroma profiles were used as targets for classification machine learning (ML) modelling. Four different ML models were developed; Models 1 (M1) and M2 based on NIR (100 inputs from 1596–2396 nm) and M3 and M4 based on the e-nose (nine sensor readings as inputs) and 19 aroma profiles as targets for all models. A customized code tested 17 artificial neural network (ANN) algorithms automatically testing performance and neuron trimming. Results showed that the Bayesian regularization algorithm was the most adequate for classification rendering precisions of M1 = 98.9%, M2 = 98.3%, M3 = 96.8%, and M4 = 96.2% without statistical signs of under- or overfitting. The proposed system can be added to robotic pourers and the brewing process at low cost, which can benefit craft and larger brewing companies.

scholarly journals A Digital Approach to Evaluate the Effect of Berry Cell Death on Pinot Noir Wines’ Quality Traits and Sensory Profiles Using Non-Destructive Near-Infrared Spectroscopy

Beverages ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 39
Author(s):  
Sigfredo Fuentes ◽  
Eden Tongson ◽  
Juesheng Chen ◽  
Claudia Gonzalez Viejo
Keyword(s):  
Cell Death ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fluorescent Imaging ◽  
Pinot Noir ◽  
Imaging Results ◽  
Commercial Vineyard ◽  
Sensory Profiles ◽  
Non Destructive

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.

scholarly journals Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence

Foods ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 33 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Eden Tongson ◽  
Damir D. Torrico ◽  
Claudia Gonzalez Viejo
Keyword(s):  
Artificial Intelligence ◽  
Water Management ◽  
Machine Learning ◽  
Management Strategies ◽  
Weather Conditions ◽  
Machine Learning Algorithms ◽  
Pinot Noir ◽  
Wine Aroma ◽  
Management Information ◽  
Artificial Neural Network Ann

Wine aroma profiles are determinant for the specific style and quality characteristics of final wines. These are dependent on the seasonality, mainly weather conditions, such as solar exposure and temperatures and water management strategies from veraison to harvest. This paper presents machine learning modeling strategies using weather and water management information from a Pinot noir vineyard from 2008 to 2016 vintages as inputs and aroma profiles from wines from the same vintages assessed using gas chromatography and chemometric analyses of wines as targets. The results showed that artificial neural network (ANN) models rendered the high accuracy in the prediction of aroma profiles (Model 1; R = 0.99) and chemometric wine parameters (Model 2; R = 0.94) with no indication of overfitting. These models could offer powerful tools to winemakers to assess the aroma profiles of wines before winemaking, which could help adjust some techniques to maintain/increase the quality of wines or wine styles that are characteristic of specific vineyards or regions. These models can be modified for different cultivars and regions by including more data from vertical vintages to implement artificial intelligence in winemaking.

Non-destructive determination of grape berry sugar concentration using visible/near infrared imaging and possible impact on wine quality

2015 ◽  
Vol 82 (12) ◽  
Author(s):  
Magali Lafontaine ◽  
Zrinka Bockaj ◽  
Maximilian Freund ◽  
Kai-Uwe Vieth ◽  
Christian Negara ◽  
...  
Keyword(s):  
Regression Model ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Pinot Noir ◽  
Hyperspectral Images ◽  
Sugar Concentration ◽  
Wine Quality ◽  
Tannin Concentration ◽  
Grape Varieties ◽  
The Impact

AbstractReducing heterogeneity in harvest material would be beneficial for wine quality and this goal may be achieved through advanced berry sorting systems. The general aim was to assess if a relationship could be found between sugar concentration and hyperspectral images. Grapes were picked at different stages of maturity and the berries were sorted according to their size and density. Hyperspectral images of the berry subsamples were obtained in the vis/NIR wavelength range with a complete spectrum from 400 nm to 2500 nm. Our results showed that vis/NIR can be used to improve the segregation of berries from all tested grape varieties based on their sugar concentration. All berries from all 12 grape varieties were used to train the regression model and the predictive power was tested on all each grape variety separately, while later validated on each variety separately, proving the possibility of using a general regression model with constant parameters to predict sugar concentration. Finally, the impact on quality was tested for red wines. Pinot noir berries with higher sugar concentrations presented more color since anthocyanin concentration was higher. Nevertheless, tannin concentration in skins and seeds tended to decrease. Berries with higher sugar concentration resulted in wines with higher anthocyanin and lower tannin concentration.

scholarly journals Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling

Foods ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 426 ◽  
Author(s):  
Gunaratne ◽  
Gonzalez Viejo ◽  
Gunaratne ◽  
Torrico ◽  
Dunshea ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Lower Cost ◽  
Physicochemical Analysis ◽  
Sensory Properties ◽  
Chemical Parameters ◽  
Chemical Fingerprinting ◽  
Physicochemical Data ◽  
Non Destructive

Chocolates are the most common confectionery and most popular dessert and snack across the globe. The quality of chocolate plays a major role in sensory evaluation. In this study, a rapid and non-destructive method was developed to predict the quality of chocolate based on physicochemical data, and sensory properties, using the five basic tastes. Data for physicochemical analysis (pH, Brix, viscosity, and color), and sensory properties (basic taste intensities) of chocolate were recorded. These data and results obtained from near-infrared spectroscopy were used to develop two machine learning models to predict the physicochemical parameters (Model 1) and sensory descriptors (Model 2) of chocolate. The results show that the models developed had high accuracy, with R = 0.99 for Model 1 and R = 0.93 for Model 2. The thus-developed models can be used as an alternative to consumer panels to determine the sensory properties of chocolate more accurately with lower cost using the chemical parameters.

scholarly journals Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  
Keyword(s):  
High Throughput ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Texture Attributes

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

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