Prediction of Acidity Levels of Fresh Roasted Coffees Using E-nose and Artificial Neural Network

Thazin, Yu; Pobkrut, Theerapat; Kerdcharoen, Teerakiat

doi:10.1109/kst.2018.8426206

Cited by 26 publications

(15 citation statements)

References 13 publications

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“…The implementation of e-noses for beer research is not new in the food science field as they have been used to assess aromas in products such as coffee acidity [22], and roasting level prediction [23], tea quality grading [24][25][26], classification of strawberry juices [24], assessment of wine smoke taint [27] and alcohol content [28], beer quality classification and aging [29][30][31], beer aroma prediction [20], meat quality and shelf life [32,33], olive oil origin and quality [33,34], milk spoilage [35], and rice infestation [36], among others. However, most of the e-noses used in those studies are expensive and need to be installed in a laboratory as they have a similar system to a gas chromatograph (GC), and/or are considered as low-cost compared to a GC, but still, cost ≥ USD 30,000 and require maintenance [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Viejo

Fuentes

2020

Fermentation

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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.

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Section: Introductionmentioning

confidence: 99%

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Viejo

Fuentes

2020

Fermentation

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“…In the case of unsupervised training, the system learns to detect similarities in unlabeled data structures [ 61 ]. The most popular tools used for pattern recognition include artificial neural networks [ 62 , 63 , 64 ], support vector machines [ 65 , 66 ], principal component analysis [ 67 , 68 ], multiple linear regression [ 69 ], generalized least square regression [ 70 , 71 ], linear discriminant, discriminant function, and stepwise discriminant analysis [ 71 , 72 , 73 ].…”

Section: Technology Used For Results Analysismentioning

confidence: 99%

Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection

Mamchur

Pekša

Clainche

et al. 2022

Sensors

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Increase in trading and travelling flows has resulted in the need for non-intrusive object inspection and identification methods. Traditional techniques proved to be effective for decades; however, with the latest advances in technology, the intruder can implement more sophisticated methods to bypass inspection points control techniques. The present study provides an overview of the existing and developing techniques for non-intrusive inspection control, current research trends, and future challenges in the field. Both traditional and developing methods, techniques, and technologies were analyzed with the use of traditional and novel sensor types. Finally, it was concluded that the improvement of non-intrusive inspection experience could be gained with the additional use of novel types of sensors (such as biosensors) combined with traditional techniques (X-ray inspection).

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“…Several learning based approaches are used in pattern recognition algorithms of ENs and are essential in improving EN systems [58] . Some frequently used tools include linear discriminant analysis (LDA) [59] , discriminant function analysis (DFA) [60] , stepwise discriminant analysis (SDA) [61] , partial least squares regression (PLSR) [62,63] , generalized least squares regression (GLSR) [64,65] , multiple linear regression (MLR) [66] , principle component analysis (PCA) [60,67] , support vector machines (SVMs) [68,69] and artificial neural networks (ANNs) [70][71][72] . The most frequently utilized data analysis algorithms for different EN sensor types are summarized in Table 2, and algorithm properties including advantages and disadvantages are given in Table 3.…”

Section: Pattern Recognitionmentioning

confidence: 99%

Electronic Nose and Its Applications: A Survey

Karakaya

Ulucan

Türkan

2019

Int. J. Autom. Comput.

256

152

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In the last two decades, improvements in materials, sensors and machine learning technologies have led to a rapid extension of electronic nose (EN) related research topics with diverse applications. The food and beverage industry, agriculture and forestry, medicine and health-care, indoor and outdoor monitoring, military and civilian security systems are the leading fields which take great advantage from the rapidity, stability, portability and compactness of ENs. Although the EN technology provides numerous benefits, further enhancements in both hardware and software components are necessary for utilizing ENs in practice. This paper provides an extensive survey of the EN technology and its wide range of application fields, through a comprehensive analysis of algorithms proposed in the literature, while exploiting related domains with possible future suggestions for this research topic.

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Prediction of Acidity Levels of Fresh Roasted Coffees Using E-nose and Artificial Neural Network

Cited by 26 publications

References 13 publications

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection

Electronic Nose and Its Applications: A Survey

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