Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging

Minced meat substitution is one of the most common frauds which not only affects consumer health but impacts their lifestyles and religious customs as well. A number of methods have been proposed to overcome these frauds; however, these mostly rely on laboratory measures and are often subject to human error. Therefore, this study proposes novel hyperspectral imaging (400–1000 nm) based non-destructive isos-bestic myoglobin (Mb) spectral features for minced meat classification. A total of 60 minced meat spectral cubes were pre-processed using true-color image formulation to extract regions of interest, which were further normalized using the Savitzky–Golay filtering technique. The proposed pipeline outperformed several state-of-the-art methods by achieving an average accuracy of 88.88%

OCM 2023 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

Novel Spectral and Spatial Process Analytical Tools for Meat Quality and Safety Assessment

Meat and meat products are highly nutritious, containing important levels of protein, vitamins, minerals and micronutrients, which are important for human growth and development. Beef has emerged as an important protein source in human diets. Minced beef is the primary ingredient for a variety of products such as burgers, meat balls, meat pastes, sausages and so on. Authenticity of the meat is a major requirement to meet the demands of consumers and assuring compliance with the government regulations and safety standards. Near-Infrared (NIR) spectroscopy and Hyperspectral Imaging (HSI) are sensing solutions which provide real time quality control and assurance. Laser Induced breakdown spectroscopy (LIBS) is an emerging technology in the area of mineral analysis in food. The unique spectral features obtained from NIR spectroscopy, HSI and LIBS make these techniques suitable for Process Analytical Technology (PAT) applications. The objective of this thesis was to evaluate the efficacy of novel spectroscopic techniques such as multi-point NIR spectroscopy, HSI and LIBS for performing quality monitoring of minced beef. A multi-point NIR system was successfully evaluated to identify and predict compositional attributes of minced beef such as moisture, fat, protein and ash; illustrating various features of the device. A HSI system was also successfully evaluated for identification and prediction of compositional attributes of minced beef along with chemical imaging. A LIBS system was successfully evaluated for: (a) quantification of minerals such as sodium (Na), potassium (K) and rubidium (Rb) in minced beef, (b) explore the potential of LIBS to detect offal adulteration and (c) demonstrate the ability of LIBS to provide spatial information of elements. Overall, the study illustrated the potential of these novel spectroscopic techniques for at/on/in-line quality monitoring of minced beef

Non-destructive Detection of Food Adulteration to Guarantee Human Health and Safety

The primary objective of this review is to critique the basic concepts of non-destructive detection of food adulteration and fraud which collectively represent a tremendous annual financial loss worldwide and a major cause of human disease. The review covers the principles of the analytical instrumentation used for the non-destructive detection of food adulteration. Examples of practical applications of these methods for the control of food adulteration are provided and a comparative analysis of the advantages and disadvantages of instrumental methods in food technology are critiqued.Целью данного обзора является критическое рассмотрение основных понятий неразрушающего выявления фальсификации и подделки продуктов питания, которые в целом вызывают огромные ежегодные финансовые убытки во всем мире и являются одной из основных причин заболеваний человечества. Материалы и методы. Литература, указанная в данном обзоре, была получена в результате поиска библиографической информации в CAB abstracts, AGRICOLA, SciFinder Scholar, Modern Language Association (MLA), American Psychological Association (APA), OECD / EEA database по инструментам, которые используются для экологической политики и управления природными ресурсами, и Web of Science.Результаты и обсуждение. Фальсификация пищевых продуктов означает преднамеренное, обманное добавление посторонних, нестандартных или дешевых ингредиентов в продукты, или разбавление или удаление некоторых ценных ингредиентов с целью увеличения прибыли. В современных условиях производители стремятся увеличить выпуск своей продукции зачастую путем изготовления и продажи некачественных и фальсифицированных продуктов.“Неразрушающее выявление фальсификации пищевых продуктов” означает анализ образца и его существенных признаков без изменения физических и химических свойств образца. Повышение качества и безопасности пищевых продуктов путем разработки научных методов обнаружения фальсификации является главным условием для поддержания здоровья потребителей. Точная объективная оценка качества и выявление фальсификации пищевых продуктов представляется важнейшей целью пищевой промышленности. В связи с совершенствованием технологии фальсификации продуктов важно быть в курсе современных, самых точных методов контроля их фальсификации. С этой целью данный обзор рассматривает основные понятия выявления фальсификации продуктов питания, принципы устройств и возможные практические применения современных методов неразрушающего выявления фальсификации продуктов питания; сравнительный анализ преимуществ и недостатков инструментальных методов, используемых в пищевых технологиях. Каждый из рассмотренных методов обсуждается с точки зрения возможных различных консистенций продуктов – газов (свободного пространства вокруг продукта), свободно текущих жидкостей (соков), мутных и вязких жидкостей (меда как продукта растительного происхождения, растительных масел) и интактных продуктов (фруктов и овощей).Выводы. Результаты, освещенные в обзоре, рекомендуется использовать при контроле качества и безопасности пищевых продуктов.Метою даного огляду є критичний розгляд основних понять неруйнівного виявлення фальсифікації і підробки продуктів харчування, які в цілому викликають величезні щорічні фінансові збитки у всьому світі і є однією з основних причин захворювань людства. Матеріали і методи. Література, зазначена в даному огляді, була отримана в результаті пошуку бібліографічної інформації в in CAB abstracts, AGRICOLA, SciFinder Scholar, Modern Language Association (MLA), American Psychological Association (APA), OECD/EEA database щодо інструментів, які використовуються для екологічної політики та управління природними ресурсами, та Web of Science. Результати та обговорення. Фальсифікація харчових продуктів означає умисне, облудне додавання сторонніх, нестандартних або дешевих інгредієнтів в продукти, або розбавлення чи видалення деяких цінних інгредієнтів з метою збільшення прибутків. У сучасних умовах виробники прагнуть збільшити випуск своєї продукції найчастіше шляхом виготовлення та продажу неякісних та фальсифікованих продуктів. “Неруйнівне виявлення фальсифікації харчових продуктів” означає аналіз зразка і його істотних ознак без зміни фізичних і хімічних властивостей зразка. Підвищення якості та безпеки харчових продуктів шляхом розробки наукових методів виявлення фальсифікації є головною умовою для підтримки здоров’я споживачів. Точна об’єктивна оцінка якості і виявлення фальсифікації харчових продуктів представляється найважливішою метою харчової промисловості. У зв’язку з удосконаленням технології фальсифікації продуктів важливо бути в курсі сучасних, найбільш точних методів контролю їх фальсифікації. З цією метою даний огляд розглядає основні поняття виявлення фальсифікації продуктів харчування, принципи пристроїв і можливі практичні застосування сучасних методів неруйнівного виявлення фальсифікації продуктів харчування; порівняльний аналіз переваг і недоліків інструментальних методів, що застосовуються в харчових технологіях. Кожен з розглянутих методів обговорюється з точки зору можливих різних консистенцій продуктів - газів (вільного простору навколо продукту), вільно текучих рідин (соків), каламутних та в'язких рідин (меду як продукту рослинного походження, рослинних масел) і інтактних продуктів (фруктів і овочів). Висновки. Результати, висвітлені в огляді, рекомендується використовувати під час контролю якості та безпеки харчових продуктів

Feasibility of utilizing color imaging and machine learning for adulteration detection in minced meat

peer-reviewedMeat products are popular foods and there is a need for cost-effective technologies for rapid quality assessment. In this study, RGB color imaging coupled with machine learning algorithms were investigated to detect plant and animal adulterants with ratios of from 1 to 50% in minced meat. First, samples were classified as either pure or adulterated, then adulterated samples were classified based on the adulterant's type. Finally, regression models were developed to predict the adulteration quantity. Linear discriminant classifier enhanced by bagging ensembling performed the best with overall classification accuracies for detecting pure or adulterated samples up to 99.1% using all features, and 100% using selected features. Classification accuracies for adulteration origin were 48.9–76.1% using all features and 63.8% for selected features. Regression trees were used for adulterant level quantification and the r (RPD) values were up to 98.0%(5.0) based on all features, and 94.5%(3.2) for selected features. Gray-level and co-occurrence features were more effective than other color channels in building classification and regression models. This study presents a non-invasive, and low-cost system for adulteration detection in minced meats

Feasibility of utilizing color imaging and machine learning for adulteration detection in minced meat

Meat products are popular foods and there is a need for cost-effective technologies for rapid quality assessment. In this study, RGB color imaging coupled with machine learning algorithms were investigated to detect plant and animal adulterants with ratios of from 1 to 50% in minced meat. First, samples were classified as either pure or adulterated, then adulterated samples were classified based on the adulterant's type. Finally, regression models were developed to predict the adulteration quantity. Linear discriminant classifier enhanced by bagging ensembling performed the best with overall classification accuracies for detecting pure or adulterated samples up to 99.1% using all features, and 100% using selected features. Classification accuracies for adulteration origin were 48.9–76.1% using all features and 63.8% for selected features. Regression trees were used for adulterant level quantification and the r (RPD) values were up to 98.0%(5.0) based on all features, and 94.5%(3.2) for selected features. Gray-level and co-occurrence features were more effective than other color channels in building classification and regression models. This study presents a non-invasive, and low-cost system for adulteration detection in minced meats

Food Recognition and Ingredient Detection Using Electrical Impedance Spectroscopy With Deep Learning Techniques to Facilitate Human-food Interactions

Food is a vital component of our everyday lives closely related to our health, well-being, and human behavior. The recent advancements of Spatial Computing technologies, particularly in Human-Food interactive (HFI) technologies have enabled novel eating and drinking experiences, including digital dietary assessments, augmented flavors, and virtual and augmented dining experiences. When designing novel HFI technologies, it is essential to recognize different food and beverages and their internal attributes (i.e., food sensing), such as volume and ingredients. As a result, contemporary research employs image analysis techniques to identify food items, notably in digital dietary assessments. These techniques, often combined with AI algorithms, analyze digital food images to extract various information about food items and quantities. However, these visual food analyzing methods are ineffective when: 1) identifying food’s internal attributes, 2) discriminating visually similar food and beverages, and 3) seamlessly integrating with people’s natural interactions while consuming food (e.g., automatically detecting the food when using a spoon to eat). This thesis presents a novel approach to digitally recognize beverages and their attributes, an essential step towards facilitating novel human-food interactions. The proposed technology has an electrical impedance measurement unit and a recognition method based on deep learning techniques. The electrical impedance measurement unit consists of the following components: 1) a 3D printed module with electrodes that can be attached to a paper cup, 2) an impedance analyzer to perform Electrical Impedance Spectroscopy (EIS) across two electrodes to acquire measurements such as a beverage’s real part of impedances, imaginary part of impedances, phase angles, and 3) a control module to configure the impedance analyzer and send measurements to a computer that has the deep learning framework to conduct the analysis. Two types of multi-task learning models (hard parameter sharing multi-task network and multi-task network cascade) and their variations (with principal component analysis and different combinations of features) were employed to develop a proof-of-concept prototype to recognize eight different beverage types with various volume levels and sugar concentrations: two types of black tea (LiptonTM and TwiningsTM English-Breakfast), two types of coffee (StarbucksTM dark roasted and medium roasted), and four types of soda (regular and diet coca-cola, and regular and diet Pepsi). Measurements were acquired from these beverages while changing volume levels and sugar concentrations to construct training and test datasets. Both types of networks were trained using the training dataset while validated with the test dataset. Results show that the multi-task network cascades outperformed the hard parameter sharing multi-task networks in discriminating against a limited number of drinks (accuracy = 96.32%), volumes (root mean square error = 13.74ml), and sugar content (root mean square error = 7.99gdm3). Future work will extend this approach to include additional beverage types and their attributes to improve the robustness and performance of the system and develop a methodology to recognize solid foods with their attributes. The findings of this thesis will contribute to enable a new avenue for human-food interactive technology developments, such as automatic food journaling, virtual flavors, and wearable devices for non-invasive quality assessment

Detection and quantification of paprika powder adulteration by near infrared (NIR) spectroscopy

Orientador: Douglas Fernandes BarbinDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de AlimentosResumo: A páprica é uma das especiarias mais consumidas no mundo, e devido aos seus atributos sensoriais desejáveis, ela apresenta um alto valor de mercado. Embora especiarias como o pó de páprica sejam usadas e consumidas apenas em pequenas quantidades, elas estão presentes em muitos alimentos processados. Em razão disso, ela se torna susceptível a adulteração por motivação econômica. Por esse motivo, muitos esforços têm sido feitos no desenvolvimento de técnicas analíticas para detecção dessas práticas fraudulentas. No entanto, muitas dessas técnicas tradicionais são destrutivas, utilizam reagentes químicos e seu uso é dispendioso e demorado. Por outro lado, técnicas de espectroscopia vibracional, aliadas a quimiometria, surgem como uma alternativa promissora na detecção de adulteração na indústria de ervas e especiarias. O uso dessas técnicas traz como vantagens a rapidez e a natureza não-destrutiva das análises. Dessa forma, a espectroscopia de infravermelho próximo (NIR) tem sido utilizada com êxito, na verificação da autenticidade e no controle de qualidade desses produtos. Diante disso, o presente trabalho teve como objetivo investigar as potencialidades da espectroscopia NIR, em conjunto com a análise multivariada, na detecção e quantificação de substâncias estranhas (fécula de batata, goma arábica e urucum), em páprica em pó. Na determinação dos níveis de adulteração, foi utilizada a regressão por mínimos quadrados parciais (PLSR). Melhores resultados da calibração PLSR foram obtidos com um número reduzido de variáveis, aplicando o método de seleção de variáveis a partir do gráfico dos coeficientes de regressão. Como resultado, para os modelos PLSR reduzidos construídos a partir dos dados espectrais de NIR, os coeficientes de determinação de predição (R2p) foram 0,960, 0,968 e 0,874 para fécula de batata, goma arábica e urucum, respectivamente e os erros quadráticos médios de predição (RMSEP) foram 1,86, 1,68 e 1,74, respectivamente. Finalmente, a análise discriminante de mínimos quadrados parciais (PLS-DA) foi o método utilizado para estabelecer um modelo de classificação para discriminar amostras de páprica adulteradas e não adulteradas e também identificar o tipo de adulteração. Assim, este método de classificação mostrou-se bastante eficiente, com especificidade maior que 90% e taxa de erro menor que 2%, para todos os modelos construídos. Os resultados obtidos neste estudo mostraram que a espectroscopia NIR, combinada com a quimiometria podem ser uteis para a rápida detecção e/ou quantificação da adulteração em páprica em póAbstract: Paprika is one of the most consumed spices in the world, and because of its desirable sensory attributes, it has a high market value. Although spices such as paprika powder are used and consumed only in small amounts, they are present in many processed foods. Because of this, it becomes susceptible to adulteration by economic motivation. For this reason, much effort has been expended in developing analytical techniques to detect such fraudulent practices. However, many of these traditional techniques are destructive, use chemical reagents and their use is expensive and time consuming. On the other hand, techniques of vibrational spectroscopy, combined with chemometrics, appear as a promising alternative in the detection of adulteration in the herb and spice industry. The use of these techniques brings as advantages the speed and the non-destructive nature of the analyses. Thus, near infrared spectroscopy (NIR) has been successfully used to verify the authenticity and quality control of these products. The objective of this study was to investigate the potential of NIR spectroscopy, in conjunction with the multivariate analysis, in the detection and quantification of foreign substances (potato starch, acacia gum and annatto) in powdered paprika. In the determination of adulteration levels, partial least squares regression (PLSR) was used. The best results of the PLSR calibration were obtained with a reduced number of variables, applying the method of selection of variables from the graph of the regression coefficients. As a result, for the reduced PLSR models built with NIR spectral data, the prediction determination coefficients (R2p) were 0.960, 0.968 and 0.874 for potato starch, acacia gum and annatto, respectively, and the mean squared errors of prediction (RMSEP) were 1.86, 1.68 and 1.74, respectively. Finally, the discriminant analysis of partial least squares (PLS-DA) was the method used to establish a classification model to discriminate adulterated and unadulterated paprika samples and also to identify the type of adulteration. Hence, this method of classification proved to be efficient, with specificity greater than 90% and error rate lower than 2%, for all models constructed. The results obtained in this study showed that NIR spectroscopy, combined with chemometrics may be useful for the rapid detection and / or quantification of paprika powder adulterationMestradoEngenharia de AlimentosMestre em Engenharia de AlimentosCAPE