Potential of front face fluorescence spectroscopy and fluorescence imaging in discriminating adulterated extra-virgin olive oil with virgin olive oil

Discrimination of male-sterility and male-fertility in Japanese cedar (Cryptomeria japonica) using near-infrared diffuse transmission spectroscopy

The increasing demand for pollen-free seedlings of Japanese cedar (Cryptomeria japonica) has created a need for a simple method to discriminate between male-sterile and male-fertile strobili. The objective of this study was to establish a classification model to quickly and easily distinguish male-sterile and male-fertile strobili in C. japonica using near-infrared (NIR) diffuse transmission spectroscopy. The absorbance spectra of C. japonica were obtained for three different months from December 2022 to February 2023 and preprocessed using three methods: untreated, smoothing, and second derivative. Principal component analysis was applied to the NIR spectra and classification models were built using a support vector machine. The sample collected in January 2023 showed the highest discrimination accuracy of 89.38% with the smoothing preprocessing, which was improved to 89.97% by limiting the wavelengths to the NIR region. Furthermore, discrimination accuracy for independent test data was evaluated by splitting the data into training and testing sets using January 2023 data with smoothing preprocessing. The discrimination accuracy for test data sets was more than 85%, and the misclassification ratio was less than 20% for each sample group. These results indicate the potential of using NIR diffuse transmission spectroscopy to discriminate between male-sterility and fertility in C. japonica.

Light-Emitting-Diode-Induced Fluorescence from Organic Dyes for Application in Excitation-Emission Fluorescence Spectroscopy for Food System Analysis

An experimental study is presented on the possibility of using the fluorescence from organic dyes as a broadband light source together with a monochromator for applications in excitation-emission matrix (EEM) fluorescence spectroscopy. A high-power single-chip light-emitting diode (LED) was chosen as an excitation source with a central output wavelength at 365 nm to excite a fluorescent solution of Coumarin 1 dye dissolved in ethanol. Two excitation configurations were investigated: direct excitation from the LED and excitation through an optical-fiber-coupled LED. A Czerny-Turner monochromator with a diffraction grating was used for the spectral tuning of the fluorescence. A simple method was investigated for increasing the efficiency of the excitation as well as the fluorescence signal collection by using a diffuse reflector composed of barium sulfate (BaSO4) and polyvinyl alcohol (PVA). As research objects, extra-virgin olive oil (EVOO), Coumarin 6 dye, and Perylene, a polycyclic aromatic hydrocarbon (PAH), were used. The results showed that the light-emitting-diode-induced fluorescence was sufficient to cover the losses on the optical path to the monochromator output, where a detectable signal could be obtained. The obtained results reveal the practical possibility of applying the fluorescence from dyes as a light source for food system analysis by EEM fluorescence spectroscopy.

Discriminating extra virgin olive oils from common edible oils: Comparable performance of PLS-DA models trained on low-field and high-field 1H NMR data

INTRODUCTION

Olive oil, derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or fraudulently mislabel oils as olive to increase profitability. Adulterated products can cause allergic reactions in sensitive individuals and can lack compounds which contribute to the perceived health benefits of olive oil, and its corresponding premium price.

OBJECTIVE

There is a need for robust methods to rapidly authenticate olive oils. By utilising machine learning models trained on the nuclear magnetic resonance (NMR) spectra of known olive oil and edible oils, samples can be classified as olive and authenticated. While high-field NMRs are commonly used for their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate for routine screening purposes. Low-field benchtop NMR presents an affordable alternative.

METHODS

We compared the predictive performance of partial least squares discrimination analysis (PLS-DA) models trained on low-field 60 MHz benchtop proton (1H) NMR and high-field 400 MHz 1H NMR spectra. The data were acquired from a sample set consisting of 49 extra virgin olive oils (EVOOs) and 45 other edible oils.

RESULTS

We demonstrate that PLS-DA models trained on low-field NMR spectra are highly predictive when classifying EVOOs from other oils and perform comparably to those trained on high-field spectra. We demonstrated that variance was primarily driven by regions of the spectra arising from olefinic protons and ester protons from unsaturated fatty acids in models derived from data at both field strengths.

Rapid identification of traditional Chinese medicines (Lonicerae japonicae flos and Lonicerae flos) and their origins using excitation-emission matrix fluorescence spectroscopy coupled with chemometrics

Lonicerae japonicae flos (LJF) and Lonicerae flos (LF) are important traditional Chinese medicine with various effects and prescription compatibility. The accurate identification of LJF and LF and their geographical origin are of great significance to the quality control and correct medication. In this work, a simple, rapid and efficient strategy for identification of Lonicerae japonicae flos and Lonicerae flos and their geographical origin was proposed by combining excitation-emission matrix fluorescence (EEMF) with chemometrics. Excitation-emission matrix fluorescence (EEMF) spectra of LJF and LF samples were characterized by parallel factor analysis (PARAFAC) to acquire chemically meaningful information. Classification models were built using three chemometric methods, including partial least squares-discrimination analysis (PLS-DA), principal component analysis-linear discriminant analysis (PCA-LDA) and random forest (RF). These models were utilized to identify LJF and LF and their geographical origin. Results revealed that PCA-LDA model gained the optimal performance with 100% classification accuracy for distinguishing between LJF and LJF from different geographical origin. Therefore, the proposed strategy could be a competitive alternative for fast and accurate differentiation of LJF and LF and their geographical origin.

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.

Honey authentication and adulteration detection using emission - excitation spectra combined with chemometrics

The aim of this study was to evaluate the usefulness of emission-excitation matrices for honey authentication and adulteration detection. For this purpose, 4 types of authentic honeys (tilia, sunflower, acacia and rape) and samples adulterated with different adulteration agents (agave, maple, inverted sugar, corn and rice in different percentages - 5%, 10% and 20%) were analysed. Each honey type and each adulteration agent exhibit unique emission-excitation spectra that can be used for the classification according to the botanical origin and for the detection of adulteration. The principal component analysis clearly separated the rape, sunflower and acacia honeys. The partial least squares - discriminant analysis (PLS-DA) and support vector machines (SVM) were used in a binary mode to separate the authentic honeys from the adulterated ones, and the SVM proved to separate much better than PLS-DA.

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.