Quality Evaluation Of Agricultural Products Research Articles

Development of a smart spectral analysis method for the determination of mulberry (Morus alba var. nigra L.) juice quality parameters using FT-IR spectroscopy.

Recently, the application of Fourier transform infrared (FT-IR) spectroscopy as a noninvasive technique combined with chemometric methods has been widely noted for quality evaluation of agricultural products. Mulberry (Morus alba var. nigra L.) is a native fruit of Iran and there is limited information about its quality characteristics. The present study aims at assessing a nondestructive optical method for determining the internal quality of mulberry juice. To do so, first, FT-IR spectra were acquired in the spectral range 1000-8333 nm. Then, the principal component analysis (PCA) was used to extract the principal components (PCs) which were given as inputs to three predictive models (support vector regression (SVR), partial least square (PLS), and artificial neural network (ANN)) to predict the internal parameters of the mulberry juice. The performance of predictive models showed that SVR got better results for the prediction of ascorbic acid (R 2= .84, RMSE=0.29), acidity (R 2= .71, RMSE=0.0004), phenol (R 2= .35, RMSE=0.19), total anthocyanin (R 2= .93, RMSE=5.85), and browning (R 2= .89, RMSE=0.062) compared to PLS and ANN. However, the ANN predicted the parameters TSS (R 2= .98, RMSE=0.003) and pH (R 2= .99, RMSE=0.0009) better than the other two models. The results indicated that a good prediction performance was obtained using the FT-IR technique along with SVR and this method could be easily adapted to detect the quality parameters of mulberry juice.

Evaluating the Efficiency of the Classifier Method When Analysing the Sales Data of Agricultural Products

Data classification as a method of input analysis is of the greatest interest and necessity for proper distribution and quality evaluation of agricultural products. The use of classification methods allows predicting whether a selected sample from the data set will fit into a particular class or group, which is necessary for the process of sorting products. This study presents the results of a comparative analysis of high-performance classifiers for assessing the effectiveness of further use in the sorting of agricultural products. The study was carried out utilising the classifiers of k-nearest neighbours, naive Bayesian classifiers, and artificial neural networks for data analysis during apple fruit sorting. It has been established that the greatest accuracy 99% of the results is demonstrated by the classifiers of k-nearest neighbours, but, at the same time, they show the lowest calculation speed (0.47 s). The best performance at any data size (65-100%) is shown by the neural network. A comprehensive review of the features and restrictions of the studied classification algorithms, as well as their applications in various areas of agriculture, has been performed.

Comparison of a novel PLS1-DA, traditional PLS2-DA and assigned PLS1-DA for classification by molecular spectroscopy

Partial least square discriminant analysis (PLS-DA) has achieved a huge success in many research systems, such as quality evaluation of agricultural products and drug analysis. However, it can be found that PLS-DA has various forms which can be mainly divided into two strategies separately called as PLS1-DA and PLS2-DA which is overused. In this work, we proposed a novel strategy not only to select representative samples for modeling, but also to build qualitative models, which is called as principal component analysis Euclidean distance PLS1-DA (PCA-EuD-PLS1-DA). Outliers were detected by the method Leverage. It is very interesting to compare the results of traditional PLS-DA tactics with the proposed method on six datasets. EuD-PLS1-DA with PCA partitioning subsets has the optimal performances than both PLS2-DA and assigned PLS1-DA when confronting multi-class problems. In particular, different classes’ tablets in NIR can be well discriminated and different classes’ tablets in Raman can be discriminated in all EuD-PLS1-DA models; and their prediction accuracy is above 98% and 80%, respectively. Their prediction accuracy for PLS2-DA is above 56% and 26%, respectively. And all RMSECV in EuD-PLS1-DA is smaller than the remaining classifiers; so does RMSEP. This behavior means EuD-PLS1-DA has a better fitness than the remaining methods. When there are only two classes in a dataset, performance of all PLS-DA models is highly similar. Before comparing the results of PLS-DA derivatives, the shortcomings in the other two methods have been systematically described. Sample selection algorithms including K–S, SPXY and PCA were compared with percentage of chosen samples in every class. And the current ratios are mainly in the area of 70–90%. Besides, ratios of every class’ samples selected for modeling are highly similar among K–S, SPXY and PCA. Most importantly, the proposed method can be realized in most statistical software.

Potential application of laser-based imaging technology in the quality evaluation of agricultural products : A review

Non-destructive quality evaluation of agricultural products particularly during postharvest stage has been a primary concern in recent years. The laser-based imaging technology is one of the most promising non-invasive tools which demonstrate potential ability to replace the conventional methods of quality monitoring that are time-consuming, expensive, laborious, inaccurate and most of all destructive. Hence, in this paper, we briefly reviewed the potential application of laser-light backscattering imaging technique (LLBI) as a non-destructive quality evaluation tool applied in agricultural products. This review mainly reports the current knowledge on the successful implementations of the LLBI in measuring the various quality-related attributes of agricultural products under different postharvest conditions such as in drying, storage, sorting, maturity identification, defect detection, etc. The basic components, uses and considerations of the technique are highlighted in this paper. Moreover, the advantages, drawbacks, measurement methods, data analysis applied as well as the accuracies obtained are briefly summarized.

Evaluation Performance of Ultrasonic Testing on Fruit Quality Determination

There are several destructive and nondestructive methods for quality evaluation of agricultural products. Most of the employed traditional techniques are time-consuming and involve considerable degree of manual works. Destructive methods provide reasonable success rate of quality determination of fruits; however, they practically have many concerns about effectiveness, time, and cost. Therefore, developing portable, fast, and cost-effective techniques without harming fruits are desired for fruit quality evaluation. This work aims to develop a complete nondestructive quality evaluation system with (a) ultrasonic testing and (b) volume estimation by automatic machine vision techniques. The ultrasonic system consisted of a programmable bipolar remote pulser unit, a couple of piezoelectric probes for ultrasonic signal acquisitions as a transmitter and a receiver, an oscilloscope, and a computer. Visual appearance (size/volume) was determined using a machine vision system based on image processing techniques. Five different images of a fruit from different angles were captured by high-resolution digital cameras. Volume of the fruit was computed after horizontal and vertical distance of the fruit’s images captured. The calculated volume values by the computer vision system are validated with the theoretical values. Although nondestructive ultrasonic estimation and volume estimation by image processing methods are cheap, fast, and practical, the results obtained in our experiments concluded that these methods are not as reliable as claimed in the literature.

Detection of hexanal in humid circumstances using hydrophobic molecularly imprinted polymers composite

Hexanal is a specific volatile organic compound (VOC) for monitoring the quality of various agricultural products and a biomarker for diagnosing lung cancer of human beings. However there is no existing hexanal gas sensor with high sensitivity and selectivity for hexanal detection in the presence of water vapor. Addressing this challenge, a quartz crystal microbalance (QCM) gas sensor modified with a hydrophobic molecularly imprinted polymers (MIPs) composite for hexanal detection was reported. Hexanal could selectively bind with the MIPs through hydrogen bonding while the hydrophobic silica nanoparticles could give the electrode resistant to water vapor. The QCM gas sensor showed the highest response to hexanal among the other tested vapors, and had little responses towards TMA, NH3, ethanol, acetone, acetic acid and diethyl ether. Octanal reached the second place followed by nonanal, but they two caused much less frequency shifts than that of hexanal. The QCM gas sensor was reliable for hexanal detection in the range of 1–80 ppm and able to detect hexanal amount from about 14.1 ppm–27.3 ppm in grass carp fillets with about 80% relative humidity in 7 days. The results displayed positive correlation with the solid phase micro extraction-gas chromatography mass spectrometer (SPME-GCMS) analysis of the same grass carp fillets samples, and the correlation coefficient was 0.98. It may offer some new ideas for gas sensors development with high sensitivity and selectivity in areas of quality evaluation of agricultural products and health evaluation of human beings.

Nondestructive Quality Evaluation of Agricultural products and Food using Near Infrared (NIR) Spectroscopy

Nondestructive Quality Evaluation of Agricultural products and Food using Near Infrared (NIR) Spectroscopy

Basic Studies on the Quality Evaluation of Agricultural Products (Part 2) The Evaluation of Color and Gloss Decrease on the Surface of Eggplants

Basic Studies on the Quality Evaluation of Agricultural Products (Part 2) The Evaluation of Color and Gloss Decrease on the Surface of Eggplants

Quality Evaluation of Agricultural Products via its Dynamic Property

Quality Evaluation of Agricultural Products via its Dynamic Property

Basic Studies on the Quality Evaluation of Agricultural Products

Basic Studies on the Quality Evaluation of Agricultural Products

農産物・食品の品質評価新技術

農産物・食品の品質評価新技術

Nondestructive Quality Evaluation of Agricultural Products: A Definition and Practical Approach

A definition of terms in this research area is presented. A general approach for applying nondestructive quality evaluation (NDQE) to horticultural crops, for both fresh and processing purposes is discussed. The approach includes consideration of the importance of biochemical, morphological and physical parameters of quality. The objective and subjective determination of single and multiple parameter judgements of quality at different stages of physiological development is a critical part of this approach. The concept of “Optimum Quality Range” is presented in relation to the development of nondestructive quality evaluation techniques and applications.

Nondestructive Quality Evaluation of Agricultural Products: Introduction

Nondestructive Quality Evaluation of Agricultural Products: Introduction

Nondestructive Quality Evaluation of Agricultural Products — Industrial Application

Use of nondestructive quality evaluation techniques for agricultural products is steadily increasing. Reasons for increased application and refinements of techniques in processing of agricultural products are discussed, especially with regard to more automation and less labor in processing, increased processing rates, and demand for more quality and uniformity of product. Information is presented on a number of nondestructive techniques presently being used, and the need for additional development is covered. Special emphasis is given to the efficiency, practicality, and economic feasibility of various techniques. Agricultural products covered include peanuts, pecans, poultry, eggs, pork, and seeds. Nondestructive techniques discussed that are used for processing these commodities into uniform, high-quality products include visual examination, weighing, screening, gravity sorting, air classification, electronic sorting, and estimation of chemical composition. A more detailed discussion of electronic color sorting of peanuts, pecans and almonds is presented, particularly in relation to aflatoxin sampling and reduction, preparing for further processing, and evaluation of commercial sorters for speed and accuracy.