Application of MOS gas sensors coupled with chemometrics methods to predict the amount of sugar and carbohydrates in potatoes

Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R2), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples

Classification and Identification of Essential Oils from Herbs and Fruits Based on a MOS Electronic-Nose Technology

The frequent occurrence of adulterated or counterfeit plant products sold in worldwide commercial markets has created the necessity to validate the authenticity of natural plant-derived palatable products, based on product-label composition, to certify pricing values and for regulatory quality control (QC). The necessity to confirm product authenticity before marketing has required the need for rapid-sensing, electronic devices capable of quickly evaluating plant product quality by easily measurable volatile (aroma) emissions. An experimental MAU-9 electronic nose (e-nose) system, containing a sensor array with 9 metal oxide semiconductor (MOS) gas sensors, was developed with capabilities to quickly identify and classify volatile essential oils derived from fruit and herbal edible-plant sources. The e-nose instrument was tested for efficacy to discriminate between different volatile essential oils present in gaseous emissions from purified sources of these natural food products. Several chemometric data-analysis methods, including pattern recognition algorithms, principal component analysis (PCA), and support vector machine (SVM) were utilized and compared. The classification accuracy of essential oils using PCA, LDA and QDA, and SVM methods was at or near 100%. The MAU-9 e-nose effectively distinguished between different purified essential oil aromas from herbal and fruit plant sources, based on unique e-nose sensor array responses to distinct, essential-oil specific mixtures of volatile organic compounds (VOCs)

A Study of Some Physical Properties of Lentil and Wild Oat Weed and Parameters Affecting the Separation of Wild Oat Weed from Lentil by a Gravity Table Separator

The present study measured physical properties of lentil and wild oat weed; mass of 1000 seeds, true density, porosity and coefficient of static friction. A gravity table separator was used to separate wild oat weeds from lentil seeds. The gravity table separator had five adjustable parameters; longitudinal slope, latitudinal slope, amplitude of oscillation, frequency of oscillation and air velocity. The effect of these parameters was investigated in order to maximize the separation of wild oat weed from lentil. Results of tests indicated that an increase in latitudinal slope of the table from 0.5° to 1° and longitudinal slope from 1.5° to 2° resulted in increased separation of wild oat weed from lentil. At longitudinal slope of 2°, latitudinal slope of 1°, amplitude of oscillation of 5 mm, frequency of oscillation of 400 cycles min-1, and air velocity of 5.7 m s-1, the separation percentage was 37%. Finally, after determination of the most suitable settings for amplitude of oscillation and air velocity; using the information on longitudinal slope, latitudinal slope and frequency of oscillation of the table was used to calculate mathematical relations of separation percentage of wild oat weed from lentil clumps using Datafit Softwar

Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively

Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning.

The activities of alpha-amylase, beta-amylase, sucrose synthase, and invertase enzymes are under the influence of storage conditions and can affect the structure of starch, as well as the sugar content of potatoes, hence altering their quality. Storage in a warehouse is one of the most common and effective methods of storage to maintain the quality of potatoes after their harvest, while preserving their freshness and sweetness. Smart monitoring and evaluation of the quality of potatoes during the storage period could be an effective approach to improve their freshness. This study is aimed at assessing the changes in the potato quality by an electronic nose (e-nose) in terms of the sugar and carbohydrate contents. Three potato cultivars (Agria, Santé, and Sprite) were analyzed and their quality variations were separately assessed. Quality parameters (i.e. sugar and carbohydrate contents) were evaluated in six 15-day periods. The e-nose data were analyzed by means of chemometric methods, including principal component analysis (PCA), linear data analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). Quadratic discriminant analysis (QDA) and multivariate discrimination analysis (MDA) offer the highest accuracy and sensitivity in the classification of data. The accuracy of all methods was higher than 90%. These results could be applied to present a new approach for the assessment of the quality of stored potatoes

Impact of periprocedural colchicine on postprocedural management in patients undergoing a left atrial appendage ligation using LARIAT

Periprocedural Colchicine for Epicardial Access Procedures Introduction Left atrial appendage (LAA) can be effectively and safely excluded using a novel percutaneous LARIAT ligation system. However, due to pericardial catheter manipulation and LAA ligation and subsequent necrosis, postprocedural course is complicated by pericarditis. We intended to evaluate the preprocedural use of colchicine on the incidence of postprocedural pericardial complications. Methods and Results In this multicenter observational study, we included all consecutive patients who underwent LARIAT procedure at the participating centers. Many patients received periprocedural colchicine at the discretion of the physician. We compared the postprocedural outcomes of patients who received prophylactic periprocedural colchicine (colchicine group) with those who did not receive colchicine (standard group). A total of 344 consecutive patients, 243 in the "colchicine group" and 101 in the "standard group," were included. The mean age, median CHADS2VASc score, and HASBLED scores were 70 ± 11 years, 3 ± 1.7, and 3 ± 1.1, respectively. There were no significant differences in major baseline characteristics between the two groups. Severe pericarditis was significantly lower in the "colchicine group" compared to the "standard group" (10 [4%] vs. 16 [16%] P<0.0001). The colchicine group, compared to the standard group, had lesser pericardial drain output (186 ± 84 mL vs. 351 ± 83, P<0.001), shorter pericardial drain duration (16 ± 4 vs. 23 ± 19 hours, P<0.04), and similar incidence of delayed pericardial effusion (4 [1.6%] to 3 [3%], P = 0.42) when compared to the standard group. Conclusion Use of colchicine periprocedurally was associated with significant reduction in postprocedural pericarditis and associated complications

Endocardial (Watchman) vs epicardial (Lariat) left atrial appendage exclusion devices: Understanding the differences in the location and type of leaks and their clinical implications

Watchman and Lariat left atrial appendage (LAA) occlusion devices are associated with LAA leaks postdeployment