Prediction of ‘Nules Clementine’ mandarin susceptibility to rind breakdown disorder using Vis/NIR spectroscopy

The use of diffuse reflectance visible and near infrared (Vis/NIR) spectroscopy was explored as a non-destructive technique to predict ‘Nules Clementine’ mandarin fruit susceptibility to rind breakdown (RBD) disorder by detecting rind physico-chemical properties of 80 intact fruit harvested from different canopy positions. Vis/NIR spectra were obtained using a LabSpec® spectrophotometer. Reference physico-chemical data of the fruit were obtained after 8 weeks of storage at 8 °C using conventional methods and included RBD, hue angle, colour index, mass loss, rind dry matter, as well as carbohydrates (sucrose, glucose, fructose, total carbohydrates), and total phenolic acid concentrations. Principal component analysis (PCA) was applied to analyse spectral data to identify clusters in the PCA score plots and outliers. Partial least squares (PLS) regression was applied to spectral data after PCA to develop prediction models for each quality attribute. The spectra were subjected to a test set validation by dividing the data into calibration (n = 48) and test validation (n = 32) sets. An extra set of 40 fruit harvested from a different part of the orchard was used for external validation. PLS-discriminant analysis (PLS-DA) models were developed to sort fruit based on canopy position and RBD susceptibility. Fruit position within the canopy had a significant influence on rind biochemical properties. Outside fruit had higher rind carbohydrates, phenolic acids and dry matter content and lower RBD index than inside fruit. The data distribution in the PCA and PLS-DA models displayed four clusters that could easily be identified. These clusters allowed distinction between fruit from different preharvest treatments. NIR calibration and validation results demonstrated that colour index, dry matter, total carbohydrates and mass loss were predicted with significant accuracy, with residual predictive deviation (RPD) for prediction of 3.83, 3.58, 3.15 and 2.61, respectively. The good correlation between spectral information and carbohydrate content demonstrated the potential of Vis/NIR as a non-destructive tool to predict fruit susceptibility to RBD

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

Monitoring biological wastewater treatment processes: Recent advances in spectroscopy applications

Biological processes based on aerobic and anaerobic technologies have been continuously developed to wastewater treatment and are currently routinely employed to reduce the contaminants discharge levels in the environment. However, most methodologies commonly applied for monitoring key parameters are labor intensive, time-consuming and just provide a snapshot of the process. Thus, spectroscopy applications in biological processes are, nowadays, considered a rapid and effective alternative technology for real-time monitoring though still lacking implementation in full-scale plants. In this review, the application of spectroscopic techniques to aerobic and anaerobic systems is addressed focusing on UV--Vis, infrared, and fluorescence spectroscopy. Furthermore, chemometric techniques, valuable tools to extract the relevant data, are also referred. To that effect, a detailed analysis is performed for aerobic and anaerobic systems to summarize the findings that have been obtained since 2000. Future prospects for the application of spectroscopic techniques in biological wastewater treatment processes are further discussed.The authors thank the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684) and the project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors also acknowledge the financial support to Daniela P. Mesquita and Cristina Quintelas through the postdoctoral Grants (SFRH/BPD/82558/2011 and SFRH/BPD/101338/2014) provided by FCT - Portugal.info:eu-repo/semantics/publishedVersio

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. In particular, those digital technologies that can be used throughout the food value chain are accurate, easy to implement, affordable, and user-friendly. Hence, this Special Issue (SI) is dedicated to novel technology based on sensor technology and machine/deep learning modeling strategies to implement artificial intelligence (AI) into food and beverage production and for consumer assessment. This SI published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, including food and beverage products, such as grapes and wine, chocolate, honey, whiskey, avocado pulp, and a variety of other food products

The synthesis of fructooligosaccharides by the fructofuranosidase FopAp from Aspergillus niger

Fructooligosaccharides (FOS) are short-chain fructans with a terminal glucose moiety and are found naturally in many plant species. Besides their wide use as an alternative sweetener in food and beverage industry, FOS have shown great potential as neutraceuticals against diabetes, colon cancer and bowel disease. The uses of FOS are dependent on the degree of polymerisation that they exhibit. β-fructofuranosidase (FFase) and fructosyltransferase (FTase) enzymes are capable of synthesing FOS from carbohydrate raw materials such as chicory and sugar beet. The aim of this study was to investigate the synthesis of FOS of a pre-defined chain length, from sucrose, by the enzyme FopAp; a β-fructofuranosidase from Aspergillus niger. ATCC 20611. The crude enzyme FopAp was successfully purified, with a yield of 78.20 %, by ammonium sulphate precipitation and anion exchange chromatography. Two protein fractions, named FA and FB were shown to exhibit FFase activity. SDS PAGE analysis revealed two proteins with molecular weights of 112 kDa and 78 kDa, which were identified as a FFase and a hydrolase. Temperature and pH optima of 20 ºC and 9, respectively, were observed for the transfructosylation activity in the FFase. The purified FFase exhibited a half life of 1.5 hrs under optimal conditions. Substrate kinetic studies indicated a high hydrolytic activity at low sucrose concentrations, with Vmax and Km of 1.25 μmol/ml/min and 3.28 mM, respectively. Analysis by response surface methodology identified temperature and pH to be significant factors for the production of kestose and nystose, at a 95 % level of confidence. These findings were confirmed by neural networks constructed to identify optimal conditions of FOS synthesis.FOS synthesis was found to be optimal between pH 6 and pH 9 at 25 ºC. The factor of reaction time was found to be insignificant within the selected experimental constraints, for both FOS species. The findings of this investigation are very important as the foundations of a commercially viable synthetic process for the production of FOS

Roadmap of cocoa quality and authenticity control in the industry: a review of conventional and alternative methods

[EN] Cocoa (Theobroma cacao L.) and its derivatives are appreciated for their aroma, color, and healthy properties, and are commodities of high economic value worldwide. Wide ranges of conventional methods have been used for years to guarantee cocoa quality. Recently, however, demand for global cocoa and the requirements of sensory, functional, and safety cocoa attributes have changed. On the one hand, society and health authorities are increasingly demanding new more accurate quality control tests, including not only the analysis of physicochemical and sensory parameters, but also determinations of functional compounds and contaminants (some of which come in trace quantities). On the other hand, increased production forces industries to seek quality control techniques based on fast, nondestructive online methods. Finally, an increase in global cocoa demand and a consequent rise in prices can lead to future cases of fraud. For this reason, new analytes, technologies, and ways to analyze data are being researched, developed, and implemented into research or quality laboratories to control cocoa quality and authenticity. The main advances made in destructive techniques focus on developing new and more sensitive methods such as chromatographic analysis to detect metabolites and contaminants in trace quantities. These methods are used to assess cocoa quality; study new functional properties; control cocoa authenticity; or detect frequent emerging frauds. Regarding nondestructive methods, spectroscopy is the most explored technique, which is conducted within the near infrared range, and also within the medium infrared range to a lesser extent. It is applied mainly in the postharvest stage of cocoa beans to analyze different biochemical parameters or to assess the authenticity of cocoa and its derivatives.The authors wish to acknowledge the financial assistance provided by the Spanish Government and European Regional Development Fund (Project RTC-2016-5241-2). Maribel Quelal Vásconez thanks the Ministry Higher Education, Science, Technology, and Innovation (SENESCYT) of the Republic of Ecuador for her PhD grant.Quelal-Vásconez, MA.; Lerma-García, MJ.; Pérez-Esteve, É.; Talens Oliag, P.; Barat Baviera, JM. (2020). Roadmap of cocoa quality and authenticity control in the industry: a review of conventional and alternative methods. Comprehensive Reviews in Food Science and Food Safety. 19(2):448-478. https://doi.org/10.1111/1541-4337.12522S448478192Abdullahi, G., Muhamad, R., Dzolkhifli, O., & Sinniah, U. R. (2018). Analysis of quality retentions in cocoa beans exposed to solar heat treatment in cardboard solar heater box. 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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

3D structures based on carbón materials and conducting polymers for electroresponsive cell cultures

149 p.El campo de la ingeniería de tejidos (TE) requiere la generación de nuevas plataformas tridimensionales como implantes o materiales tridimensionales para estudios de modelos. Dentro de todos los tejidos, nosotros nos hemos enfocado en aquellos que se ven favorecidos cuando están crecidos sobre un entorno electroactivo, tales como el tejido neuronal o cardíaco. Para que estas estructuras cumplan todos los requisitos y mimeticen el tejido nativo se requieren propiedades mecánicas blandas, conductividad, porosidad controlada y biocompatibilidad.Esta tesis doctoral ha abordado el desafío de fabricar estructuras 3D con el polímero conductor PEDOT, que carece de posibilidad de formar estructuras 3D por él mismo, usando otros materiales auxiliares para conseguirlo. De esta manera, en el primer y segundo capítulo de la tesis se han desarrollado estructuras tidimensionales de PEDOT y nanotubos de carbono (CNT) usando metodologías comúnmente utilizadas para formar estructuras bidimensionales tipo films. Estas estructuras han sido caracterizadas mostrando excelente conductividad, propiedades mecánicas ideales para cultivo neuronal, porosidad y buena biocompatibilidad. Por último, se ha diseñado un material conductor e imprimible por impresión 3D, formado por un poliéster común (PLA) y PEDOT. Ambos en conjunto poseen buena biocompatibilidad y la posibilidad de madurar tejido cardíaco, generando durante su co-cultivo estructuras de la matriz extracelular que permiten al cardiomiocito latir y por lo tanto mantener su funcionalidad.Polymat CICbiomaGUN