Analytical-performance improvement of laser-induced breakdown spectroscopy for the processing degree of wheat flour using a continuous wavelet transform

Diagnosis of HLB-asymptomatic citrus fruits by element migration and transformation using laser-induced breakdown spectroscopy

Huanglongbing (HLB) is one of the most devastating bacterial diseases in citrus growth and there is no cure for it. The mastery of elemental migration and transformation patterns can effectively analyze the growth of crops. The law of element migration and transformation in citrus growth is not very clear. In order to obtain the law of element migration and transformation, healthy and HLB-asymptomatic navel oranges collected in the field were taken as research objects. Laser-induced breakdown spectroscopy (LIBS) is an atomic spectrometry technique for material component analysis. By analyzing the element composition of fruit flesh, peel and soil, it can know the specific process of nutrient exchange and energy exchange between plants and the external environment, as well as the rules of internal nutrient transportation, distribution and energy transformation. Through the study of elemental absorption, the growth of navel orange can be effectively monitored in real time. HLB has an inhibitory effect on the absorption of navel orange. In order to improve the detection efficiency, LIBS coupled with SVM algorithms was used to distinguish healthy navel oranges and HLB-asymptomatic navel oranges. The classification accuracy was 100%. Compared with the traditional detection method, the detection efficiency of LIBS technology is significantly better than the polymerase chain reaction method, which provides a new means for the diagnosis of HLB-asymptomatic citrus fruits.

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

Determination of the nutrient profile in plant materials using laser-induced breakdown spectroscopy with partial least squares-artificial neural network hybrid models

Nutrient profile determination for plant materials is an important task to determine the quality and safety of the human diet. Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectrometry of the material component analytical technique. However, quantitative analysis of plant materials using LIBS usually suffers from matrix effects and nonlinear self-absorption. To overcome this problem, a hybrid quantitative analysis model of the partial least squares-artificial neural network (PLS-ANN) was used to detect the compositions of plant materials in the air. Specifically, fifty-eight plant materials were prepared to split into calibration, validation and prediction sets. Nine nutrient composition profiles of Mg, Fe, N, Al, B, Ca, K, Mn, and P were employed as the target elements for quantitative analysis. It demonstrated that the prediction ability can be significantly improved by the use of the PLS-ANN hybrid model compared to the method of standard calibration. Take Mg and K as examples, the root-mean-square errors of calibration (RMSEC) of Mg and K were decreased from 0.0295 to 0.0028 wt.% and 0.2884 to 0.0539 wt.%, and the mean percent prediction errors (MPE) were decreased from 5.82 to 4.22% and 8.82 to 4.12%, respectively. This research provides a new way to improve the accuracy of LIBS for quantitative analysis of plant materials.

Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network

A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.

Determination of antimony in soil using laser-induced breakdown spectroscopy assisted with laser-induced fluorescence

Antimony (Sb) in soil is attracting attention in the research community due to its potential toxicity and carcinogenicity. Traditional methods of detecting Sb lack the ability of rapid and nondigestion analysis, which hinders their development and application. Moreover, it is still a challenge for laser-induced breakdown spectroscopy (LIBS) to detect Sb in soil due to the weak intensities and intense interference of spectral lines. Here, LIBS, assisted with laser-induced fluorescence (LIBS-LIF), was used to selectively enhance the Sb's characteristic spectral lines under optimal parameters. The quantitative analysis performance was notably improved with a determination coefficient (R²) of 0.991, the limit of detection of 0.221 μg/g, and root mean square error of cross validation of 3.592 μg/g. These results demonstrate that LIBS-LIF has the potential to realize the rapid and accurate analysis of Sb in soil.

Identification of Huanglongbing-infected navel oranges based on laser-induced breakdown spectroscopy combined with different chemometric methods

In order to realize rapid identification of Gannan navel oranges infected by Huanglongbing (HLB), a full optical diagnostic method of laser-induced breakdown spectroscopy (LIBS) was proposed. All navel oranges were collected from Ganzhou, Jiangxi, China, and samples contain healthy and HLB-infected navel oranges. The LIBS spectra of the plasma plume were collected directly from the epidermis of these navel oranges. The navel orange LIBS spectra in the wavelength range of 200-1050 nm were pretreated with smoothing and multiple scatter correction; on the basis of 10×10-fold cross validation, a random forest (RF) model based on continuous wavelet transform (CWT) and principal component analysis (PCA) were analyzed to identify the navel orange of HLB. The results showed that the PCA-RF and CWT-RF models coupled with suitable methods in preprocessing data can identify HLB-infected navel oranges. The average accuracy obtained from the CWT-RF model was 96.86% in the training set and 97.45% in the test set; the average accuracy by the PCA-RF model was 97.64% in the training set and 97.89% in the test set. The overall results demonstrate that LIBS combined with CWT-RF or PCA-RF, as a valuable analytical tool, could be used for HLB-infected navel orange identification.

Laser-induced breakdown spectroscopy assisted chemometric methods for rice geographic origin classification

The problems of adulteration and mislabeling are very common in the food industry. Laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods has many intrinsic advantages on adulteration analysis of various materials. In this work, several chemometric algorithms, i.e., principal component analysis (PCA), decision tree (DT), random forest (RF), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM), were carried out assisted by LIBS technology to study the classification performances on rice geographic origins. A series of samples, including 20 kinds of rice samples from different geographic origins, was detected using LIBS with no pretreatment processes. For data analysis, PCA was employed to reduce the input variables, and to reduce the collinearity of LIBS spectral results as well. The results showed the classification accuracies of the mentioned chemometric algorithms of DT, RF, PLS-DA, LDA, and SVM with 89 input variables of 86.80%, 96.30%, 96.80%, 98.60%, and 99.20%, respectively. At the same time, the operation times of these algorithms were 3.81 s, 54.64 s, 3.63 s, 2.09 s, and 531.01 s, respectively. On the other hand, 30 principal components of input variables were also tested under the same conditions. The classification accuracies for the above algorithms were 81.60%, 98.00%, 95.70%, 98.40%, and 99.20%, respectively. The operation times were 2.01 s, 4.88 s, 3.67 s, 0.36 s, and 308.55 s, respectively. In addition, the five-fold cross-validation classification accuracies with 30 input variables for DT, RF, PLS-DA, LDA, and SVM were 83.75%, 97.95%, 94.75%, 98.35%, and 99.25%, respectively. As a result, LDA was demonstrated to be the best and most efficient tool for rice geographic origin classification assisted by LIBS with high accuracy and analytical speed, which has great potential for rapid identification of adulterated products in agriculture without use of any chemical reagent.