Improving the Efficiency of Rice Drying: Impact of Operational Variables on the Drying Rate and Quality of a South American Variety

A key challenge for the rice industry during harvest is to improve the efficiency of the drying process, which involves increasing the drying rate and the head rice yield (HRY). In the present chapter, the main variables affecting the efficiency of rice drying were discussed. Then, the impact of the drying air conditions on the drying efficiency of a long-grain South American rice variety at different rice moisture contents (MC) was studied using a thin-layer lab-scale dryer. Drying at each air condition was modeled using Page’s equation. The drying rate increased as the air conditions became more extreme (higher temperature or lower relative humidity). The effect on the HRY depended on the rice MC. Therefore, a two-stage drying program was proposed using different drying air temperatures depending on rice MC. These results were applied to create a drying program for a long-grain South American variety dried in a cross-flow commercial dryer. The two programs tested increased the drying rate and one of them also increased the HRY, compared to drying at the industry operational conditions. Implementing this program would improve the efficiency of the drying process, increasing the reception capacity and the profitability of the rice obtained

Efecto de la variedad y de la humedad de cosecha en la temperatura de transición vítrea de variedades uruguayas de arroz = Effect of rice variety and harvest moisture content on the glass transition temperature of Uruguayan rice varieties

La temperatura de transición vítrea (Tg) de un material es el rango de tempe-raturas donde ocurre la transición entre un estado cauchoso y un estado vítreo, produciéndose cambios que se ven reflejados en sus propiedades fisicoquímicas. El objetivo de este trabajo es determinar las curvas de transición vítrea (Tg vs. Humedad de grano) de tres variedades uruguayas de arroz y evaluar si existen di-ferencias significativas entre ellas. Se estudia también la influencia de la humedad de cosecha (HC). Para ello, cada muestra de arroz se seca por diferentes períodos de tiempo para obtener humedades de grano (HG) en un rango entre 10% y 22% (en base húmeda). A continuación, se mide la Tg por calorimetría diferencial de barrido (DSC). Los resultados se comparan aplicando ANOVA y el test de Tukey. Se observó que la Tg aumenta a medida que disminuye la HG para las tres variedades estudiadas. Las curvas de transición vítrea muestran que existen diferencias significativas entre las tres variedades en el rango de HG de 12% a 16%. La HC no afectó la Tg en todo el rango estudiado. Los resultados obtenidos pueden aplicarse para optimizar el proceso de secado del arroz, minimizando la formación de fisura

Caracterização do perfil sensorial e avaliação da tipicidade dos vinhos tintos DOP Bairrada e IGP Beira Atlântico

The distinctive Portuguese wines from Beira Atlântico region, encompassing the protected designation of origin (PDO) Bairrada and the protected geographical indication (PGI) Beira Atlântico, were investigated by a wine expert panel through descriptive analysis and through assessment of typicality. For that 19 trained tasters performed a blind sensory evaluation of 21 representative wines from those designations. The variables considered were the color tonality and color intensity, aroma intensity, 18 aroma descriptors and 14 taste descriptors. Typicality was investigated through a single question, where the assessor was asked to score if the sample is a good or bad example of the type. Of the 21 wines selected, seven were classified as PGI or Regional, eight as PDO and six as “Clássico”. Firstly, differences were analyzed between wine types considering all variables followed by clusters analyses confirmation. We could not find any difference between Regional and PDO Bairrada wines in terms of typicality and sensory profile. However the small group of “Clássico” wine was clearly identified by the tasters as being more typical, with also significant differences on sensory evaluation. Secondly, centered means analysis (CMA) of the 18 aroma and 14 taste items were performed to identify which of them are considered to be more distinctive. Thirdly, an exploratory factor analyses (EFA) by the principal component method (PCM) was applied to data, allowing identification of five vectors which aggregate the aroma items and four vectors which aggregate taste items. Finally, data collected from a sample of 20 questionnaires from a previous study based on cognitive knowledge and long term memory of 20 wine experts interviewed over the same 18 aroma variables and 14 taste variables, was analyzed under the same principal components (PC) and compared. The 21 wines representatives of PGI Beira Atlântico and PDO Bairrada can be defined as being medium-high intensity, ruby colored wines, having a woody & spice, ripe fruit aroma profile with also herbal and mineral aromas. On taste they tend to have a pronounced component of acidity & astringency, balanced with a smooth & sweet taste component, with a very persistent finishinfo:eu-repo/semantics/publishedVersio

Potential of blanquilla pear variety to produce pear spirits:influence of the fermentation and distillation conditions in the final quality of the spirits

The research focused on three main aspects of the spirit production process: the fermentation conditions, the distillation conditions and the raw material used. The influence of each of these aspects on the quality of the distillates obtained therefrom was studied by detecting and quantifying the main flavor compounds. The chemical analyses showed that the fermentation yeast, temperature and pH had a significant effect on the volatile composition of the pear spirits obtained. However, sensory evaluations showed no significant differences between some of these samples. As far as the distillation process is concerned, the aromatic compounds quantified indicated that the quality of the distillates produced by the distillations in a copper alembic were better than the distillates produced in glass devices and in glass devices with copper shavings. In addition, the distillations performed in the presence of the fermentation lees usually gave better quality distillates. The raw material used also affected the volatile composition of the distillates. Sensorial analysis showed that distillates from natural pear juice were preferred to distillates from pear juice concentrate.El presente trabajo de investigación se centró en tres aspectos del proceso productivo de los aguardientes de pera: las condiciones de fermentación, las condiciones de destilación y la materia prima utilizada. La influencia de cada uno de ellos en la calidad de las bebidas destiladas obtenidas se estudió por medio de la detección y cuantificación de los principales compuestos volátiles utilizando cromatografía gaseosa. Los análisis realizados mostraron que la levadura utilizada, la temperatura, y el pH de fermentación, poseen un efecto significativo en la composición aromática de los destilados obtenidos. Sin embargo, el análisis sensorial no mostró diferencias significativas entre algunos de ellos. Respecto a las condiciones de destilación, y basándose en la composición aromática obtenida, se puede decir que los destilados de mejor calidad fueron los obtenidos con alambique de cobre (frente a los obtenidos en equipo de vidrio y en equipo de vidrio con virutas de cobre). Además, las destilaciones realizadas en presencia de las lías de fermentación en general dieron destilados de mejor calidad. Por otra parte, la materia prima utilizada también afectó la composición volátil de los destilados. El análisis sensorial realizado mostró que los destilados obtenidos a partir de zumo natural de pera eran preferidos frente a los obtenidos a partir de zumo concentrado

Dynamic modeling and simulation of an alembic pear wine distillation

10.1016/j.fbp.2013.04.00

Efeito da variedade e da humidade de colheita na temperatura de transição vítrea de variedades uruguaias de arroz

The glass transition temperature (Tg) of a material is the range of temperatures where the transition between a glassy and a rubbery state occurs, producing changes that are reflected in its physicochemical properties. The objective of this research is to determine the glass transition curves (Tg vs. grain moisture content) of three Uruguayan rice varieties and assess whether there are significant differences among them. The influence of the harvest moisture content (HMC) is also evaluated. To do this, each rice sample is dried for different periods of time to obtain grain moisture contents (GMC) in a range between 10% and 22% (on a wet basis). The Tg is then measured by differential scanning calorimetry (DSC). The results are compared by applying ANOVA and the Tukey test. It was observed that the Tg increases as GMC decreases for the three varieties studied. The glass transition curves show that there are significant differences among the three varieties in the range of GMC going from 12% to 16%. The HMC did not affect the Tg in the range studied. The results obtained can be applied to optimize the rice drying process, minimizing the formation of fissures.La temperatura de transición vítrea (Tg) de un material es el rango de temperaturas donde ocurre la transición entre un estado cauchoso y un estado vítreo, produciéndose cambios que se ven reflejados en sus propiedades fisicoquímicas. El objetivo de este trabajo es determinar las curvas de transición vítrea (Tg vs. Humedad de grano) de tres variedades uruguayas de arroz y evaluar si existen diferencias significativas entre ellas. Se estudia también la influencia de la humedad de cosecha (HC). Para ello, cada muestra de arroz se seca por diferentes períodos de tiempo para obtener humedades de grano (HG) en un rango entre 10% y 22% (en base húmeda). A continuación, se mide la Tg por calorimetría diferencial de barrido (DSC). Los resultados se comparan aplicando ANOVA y el test de Tukey. Se observó que la Tg aumenta a medida que disminuye la HG para las tres variedades estudiadas. Las curvas de transición vítrea muestran que existen diferencias significativas entre las tres variedades en el rango de HG de 12% a 16%. La HC no afectó la Tg en todo el rango estudiado. Los resultados obtenidos pueden aplicarse para optimizar el proceso de secado del arroz, minimizando la formación de fisuras.A temperatura de transição vítrea (Tg) de um material é o intervalo de temperaturas em que ocorre a transição entre um estado borrachoso e um estado vítreo, produzindo-se alterações que se refletem em suas propriedades físico-químicas. O objetivo deste trabalho é determinar as curvas de transição vítrea (Tg x Umidade de grão) de três variedades de arroz uruguaias e avaliar se há diferenças significativas entre elas. A influência da umidade de colheita (UC) também é estudada. Para isso, cada amostra de arroz é secada por diferentes períodos de tempo para obter umidades de grão (UG) em um intervalo entre 10% e 22% (em base úmida). A seguir, a Tg é medida por calorimetria de varredura diferencial (DSC). Os resultados são comparados aplicando ANOVA e o teste de Tukey. Observou-se que a Tg aumenta à medida que a UG diminui nas três variedades estudadas. As curvas de transição vítrea mostram que há diferenças significativas entre as três variedades no intervalo de UG de 12% a 16%. A UC não afetou a Tg em todo o intervalo estudado. Os resultados obtidos podem ser aplicados para otimizar o processo de secagem do arroz, minimizando a formação de fissuras

Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection

Geographical product recognition has become an issue for researchers and food industries. One way to obtain useful information about the fingerprint of wines is by examining that fingerprint’s chemical components. In this paper, we present a data mining and predictive analysis to classify Brazilian and Uruguayan Tannat wines from the South region using the support vector machine (SVM) classification algorithm with the radial basis kernel function and the F-score feature selection method. A total of 37 Tannat wines differing in geographical origin (9 Brazilian samples and 28 Uruguayan samples) were analyzed. We concluded that given the use of at least one anthocyanin (peon-3-glu) and the radical scavenging activity (DPPH), the Tannat wines can be classified with 94.64% accuracy and 0.90 Matthew’s correlation coefficient (MCC). Furthermore, the combination of SVM and feature selection proved useful for determining the main chemical parameters that discriminate with regard to the origin of Tannat wines and classifying them with a high degree of accuracy. Additionally, to our knowledge, this is the first study to classify the Tannat wine variety in the context of two countries in South America

Using Support Vector Machines and neural networks to classify Merlot wines from South America

Wines with a clear geographical origin are an issue of interest for consumers and food industries. This paper presents a data mining study of Merlot wines from South America to identify the fingerprint of their geographical origin. A group of samples from Argentina (n = 17), Brazil (n = 12), Chile (n = 48), and Uruguay (n = 6) was analyzed. Twenty chemical compounds were determined by high-performance liquid chromatography (HPLC). These compounds include antioxidant activity, total polyphenols, total anthocyanins, individual anthocyanins and color. Four binary classification problems were performed (Brazil versus non-Brazil, Argentina versus non-Argentina, Chile versus non-Chile, and Uruguay versus non-Uruguay) to investigate the geographic characteristics of each country. Through the evaluation of binary classifications in our dataset it was possible to identify the main variables (chemical compounds) that discriminate between the countries. We used the following algorithms: Synthetic Minority over-sample Technique and under-sampling to balance the dataset of each classification approach, the Relief algorithm to obtain a variable importance ranking and the classifiers Support Vector Machines, Multilayer Perceptron and Radial Basis Function Network with dynamic decay adjustment. SVM model obtained the highest performance measures among the classifiers for each dataset (93.73% of accuracy for the Brazil versus non-Brazil, 91.18% for the Argentina versus non-Argentina, 79.16% for the Chile versus non-Chile, and 91.67% for the Uruguay versus non-Uruguay classification). These accuracies were achieved by the search of the possible variable subsets according to Relief for each classification approach. We found that some variables, such as DPPH, wine color and individual anthocyanins, are among the most important variables in the characterization of Merlot wines. Keywords: Support Vector Machine, Multilayer Perceptron, Anthocyanins, Feature selection, Merlot wines, South America wine