Tea classification and quality assessment using laser-induced fluorescence and chemometric evaluation

Toward commercial applications of LED and laser-induced fluorescence techniques for food identity, quality, and safety monitoring: A review

The assessment of food safety and quality is a matter of paramount importance, especially considering the challenges posed by climate change. Convenient, eco-friendly, and non-destructive techniques have attracted extensive attention in the food industry because they can retain food safety and quality. Fluorescence radiation, the process by which fluorophore emits light upon the absorption of ultraviolet or visible light, offers the advantages of high sensitivity and selectivity. The use of excitation-emission matrix (EEM) has been extensively explored in the food industry, but on-site detection of EEMs remain a challenge. To address this limitation, laser-induced fluorescence (LIF) and light emitting diode-induced fluorescence (LED-IF) have been implemented in many cases to facilitate the transition of fluorescence measurements from the laboratory to commercial applications. This review provides an overview of the application of commercially available LIF/LED-IF devices for non-destructive food measurement and recent studies that focus on the development of LIF/LED-IF devices for commercial applications. These studies were categorized into two stages: the preliminary exploration stage, which emphasizes the selection of an appropriate excitation wavelength based on the combination of EEM and chemometrics, and the pre-application stage, where experiments were conducted on scouting with specific excitation wavelength. Although commercially available devices have emerged in many research fields, only a limited number have been reported for use in the food industry. Future studies should focus on enhancing the diversity of test samples and parameters that can be measured by a single device, exploring the application of LIF techniques for detecting low-concentration substances in food, investigating more quantitative approaches, and developing embedded computing devices.

Detection of the Adulteration of Motor Oil by Laser Induced Fluorescence Spectroscopy and Chemometric Techniques

Petroleum products are the target of fraudulent practices due to their high commercial value. The aim of this study is to provide a new analysis system to assess motor oil adulteration. For this purpose, Laser Induced Fluorescence (LIF) spectroscopy was exploited coupled with chemometric tools to detect motor oil adulteration by three types of cheap motor oils. Principal Component Analysis (PCA) was able to distinguish samples in three groups according to the type of adulterant. Besides, Partial Least Squares Regression (PLSR) was exploited to determine the percentage of adulteration. The best model was obtained with a regression coefficient of 0.96, Root Mean Square Error of Prediction (RMSEP) of 2.83, Standard Error of Prediction (SEP) of 2.83 and Bias of 0.40. The main results of this work provide new analysis system using the combination of LIF spectroscopy combined to PCA and PLS as an efficient and fast method for motor oil analysis.

Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks

The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.

Quantitative Detection of Extra Virgin Olive Oil Adulteration, as Opposed to Peanut and Soybean Oil, Employing LED-Induced Fluorescence Spectroscopy

As it is high in value, extra virgin olive oil (EVOO) is frequently blended with inferior vegetable oils. This study presents an optical method for determining the adulteration level of EVOO with soybean oil as well as peanut oil using LED-induced fluorescence spectroscopy. Eight LEDs with central wavelengths from ultra-violet (UV) to blue are tested to induce the fluorescence spectra of EVOO, peanut oil, and soybean oil, and the UV LED of 372 nm is selected for further detection. Samples are prepared by mixing olive oil with different volume fractions of peanut or soybean oil, and their fluorescence spectra are collected. Different pre-processing and regression methods are utilized to build the prediction model, and good linearity is obtained between the predicted and actual adulteration concentration. This result, accompanied by the non-destruction and no pre-treatment characteristics, proves that it is feasible to use LED-induced fluorescence spectroscopy as a way to investigate the EVOO adulteration level, and paves the way for building a hand-hold device that can be applied to real market conditions in the future.

Ripening of avocado fruits studied by spectroscopic techniques

Avocados are considered very healthy due to the high content mono-unsaturated lipid, essential vitamins and minerals, minimal sugar and no cholesterol and are therefore sometimes referred to as "the perfect fruits". Avocados, mainly grown in Latin-America, are harvested unripe and sent overseas. However, the ripening process is very difficult to assess visually and tactilely. A tool for precise noninvasive judgment of the status would be valuable as the fruit is too expensive to be cut open unripe or overdue. A white-light source and a light-emitting diode unit with four excitation wavelengths (365, 385, 395, and 405 nm) were used for reflectance and fluorescence spectroscopy in a fiber-coupled set-up for noninvasive monitoring. Twelve non-ripe avocados, with approximately the same size and appearance, were studied and divided into three groups and kept at three different storage conditions; at room temperature, in a refrigerator and a combination of the two. We showed that fluorescence was useful for following the ripening process. A method, which compensates for the spatial variations in spectral properties around a fruit, is described. Remote fluorescence monitoring, intended for orchard use, was also demonstrated. A low-cost device based on fluorescence for avocado ripeness assessment is proposed.

Fluorescence Scheimpflug LiDAR developed for the three-dimension profiling of plants

This work proposes a novel fluorescence Scheimpflug LiDAR (SLiDAR) technique based on the Scheimpflug principle for three-dimension (3D) plant profile measurements. A 405 nm laser diode was employed as the excitation light source to generate a light sheet. Both the elastic and inelastic/fluorescence signals from a target object (e.g., plants) can be simultaneously measured by the fluorescence SLiDAR system employing a color image sensor with blue, green and red detection channels. The 3D profile can be obtained from the elastic signal recorded by blue pixels through elevation scanning measurements, while the fluorescence intensity of the target object is mainly acquired by red and green pixels. The normalized fluorescence intensity of the red channel, related to the chlorophyll distribution of the plant, can be utilized for the classification of leaves, branches and trunks. The promising results demonstrated in this work have shown a great potential of employing the fluorescence SLiDAR technique for 3D fluorescence profiling of plants in agriculture and forestry applications.

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.

Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

In order to rapidly and nondestructively identify tea grades, fluorescence hyperspectral imaging (FHSI) technology was proposed in this paper. A total of 309 Tieguanyin tea samples with three different grades were collected and the fluorescence hyperspectral data was acquired by hyperspectrometer (400 to 1000 nm). The characteristic wavelengths were respectively selected by Bootstrapping Soft Shrinkage (BOSS), Variable Iterative Space Shrinkage Approach (VISSA) and Model Adaptive Space Shrinkage (MASS) algorithms. Then, Support Vector Machine (SVM) was applied to establishing the relationship between the characteristic peaks, the full spectra, three characteristic spectra and the labels of tea grades. The results showed that VISSA-SVM model had the best classification performance, but the model precision can still be improved. Thus, Artificial Bee Colony (ABC) algorithm was introduced to optimize the parameters of SVM model. The accuracy and Kappa coefficient of test set of VISSA-ABC-SVM model were improved to 97.436% and 0.962, respectively. Therefore, the combination of fluorescence hyperspectra with VISSA-ABC-SVM model can accurately identify the grade of Tieguanyin tea. PRACTICAL APPLICATION: The rapid and accurate nondestructive tea grade identification method contributes to the construction of the tea online grade detection system. FHSI technology can solve the shortcomings of the reported methods and improved the identification accuracy of tea grades. It can be applied to the rapid detection of tea quality by tea companies, tea market, tea farmers and other demanders.

Tea types classification with data fusion of UV-Vis, synchronous fluorescence and NIR spectroscopies and chemometric analysis

The potential of selected spectroscopic methods - UV-Vis, synchronous fluorescence and NIR as well a data fusion of the measurements by these methods - for the classification of tea samples with respect to the production process was examined. Four classification methods - Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA) and Support Vector Machine (SVM) - were used to analyze spectroscopic data. PCA analysis was applied prior to classification methods to reduce multidimensionality of the data. Classification error rates were used to evaluate the performance of these methods in the classification of tea samples. The results indicate that black, green, white, yellow, dark, and oolong teas, which are produced by different methods, are characterized by different UV-Vis, fluorescence, and NIR spectra. The lowest error rates in the calibration and validation data sets for individual spectroscopies and data fusion models were obtained with the use of the QDA and SVM methods, and did not exceed 3.3% and 0.0%, respectively. The lowest classification error rates in the validation data sets for individual spectroscopies were obtained with the use of RDA (12,8%), SVM (6,7%), and QDA (2,7%), for the UV-Vis, SF, and NIR spectroscopies, respectively. NIR spectroscopy combined with QDA outperformed other individual spectroscopic methods. Very low classification errors in the validation data sets - below 3% - were obtained for all the data fusion data sets (SF + UV-Vis, SF + NIR, NIR + UV-Vis combined with the SVM method). The results show that UV-Vis, fluorescence and near infrared spectroscopies may complement each other, giving lower errors for the classification of tea types.

Optical characterization of Chinese hybrid rice using laser-induced fluorescence techniques-laboratory and remote-sensing measurements

Chinese hybrid rice of different varieties, growing in paddies in the Pingqiao district, north of Xinyang city, Henan province, China, was studied in detailed spectroscopic characteristics using laser-induced fluorescence. The base for the studies was the new South China Normal University mobile lidar laboratory, which was dispatched on site, providing facilities both for laboratory studies using a 405 nm excitation source as well as remote sensing measurements at ranges from around 40 m-120 m, mostly employing the 532 nm output from a Nd:YAG laser. We, in particular, studied the spectral influence of the species varieties as well as the level of nitrogen fertilization supplied. Specially developed contrast functions as well as multivariate techniques with principal components and Fisher's discriminate analyses were applied, and useful characterization of the rice could be achieved. The chlorophyll content mapping of the 30 zones was obtained with the remote sensing measurements.

Oil pollution discrimination by an inelastic hyperspectral Scheimpflug lidar system

An inelastic hyperspectral Scheimpflug lidar system is developed for range-resolved oil pollution detection and discrimination. A theory of system parametric design is built for aquatic circumstances, and laser-induced fluorescence spectra with an excitation wavelength of 446 nm are employed to detect oil pollution. Seven kinds of typical oil samples are tested and well distinguished using the principal component analysis (PCA) and linear discriminant analysis (LDA) methods. It has been shown that blue laser diodes (LD) have great potential for oil pollution detection, and our system could be further utilized for more applications in both marine and terrestrial environments.

Realistic Instrumentation Platform for Active and Passive Optical Remote Sensing

We describe the development of a novel versatile optical platform for active and passive remote sensing of environmental parameters. Applications include assessment of vegetation status and water quality. The system is also adapted for ecological studies, such as identification of flying insects including agricultural pests. The system is based on two mid-size amateur astronomy telescopes, continuous-wave diode lasers at different wavelengths ranging from violet to the near infrared, and detector facilities including quadrant photodiodes, two-dimensional and line scan charge-coupled device cameras, and a compact digital spectrometer. Application examples include remote Ramanlaser-induced fluorescence monitoring of water quality at 120 m distance, and insect identification at kilometer ranges using the recorded wing beat frequency and its spectrum of overtones. Because of the low cost this developmental platform is very suitable for advanced research projects in developing countries and has, in fact, been multiplied during hands-on workshops and is now being used by a number of groups at African universities.