Portable NIR spectroscopy and one-class modeling for authentication of buckwheat flour

The feasibility of near-infrared (NIR) diffuse reflectance spectroscopy scanning from 1100 to 2500 nm in the analyses of the amounts of moisture, protein, starch, amylose, and tannin in buckwheat flours was examined. Fifty ground samples comprised of 27 different cultivars harvested in 12 different countries were divided into two sets: 35 samples as a calibration set and 15 samples as a prediction set. The multiple regression equations (MREs) established between the second derivative NIR spectra data and the reference data, which were obtained by chemical analyses of the calibration set, gave multiple correlation coefficients of higher than 0.93 for moisture, protein, and starch, and workable standard error of predictions (SEPs) in relation to the reference standard deviation data. In contrast, the MREs for amylose and tannin were judged as unstable because the SEPs had no difference with their reference standard deviation data. The influential wavelengths were 1925 nm for water (assigned to the combination of the stretching and bending vibrations of hydroxyl [OH]), 2057 nm for protein (assigned to the combination of NH and amide II or III), and 2100 nm for starch (assigned to the combination of OH and CO). The obtained results indicated that the NIR procedure can be used as a nondestructive analysis method for rapid and simple measurement of the amounts of moisture, protein, and starch in buckwheat flours.

In this study, two of the most common drugs (Cialis ® and Viagra ® ) that are subjected to adulteration were investigated. The investigation involved examining the above drugs using both portable NIR and UV-Visible spectroscopy. The obtained spectral data were analyzed using multivariate chemometric methods such as Principal Components Analysis (PCA) and Cluster Analysis (CA). Discriminating between the original and the counterfeit drugs was successfully demonstrated with no or little chemical pretreatment. Detection of counterfeit drugs from different sources was also possible.

Near Infrared Spectroscopy (NIRS) has been widely used to access the brain functional activity non-invasively. A portable, multi-channel, continuous-wave (CW) NIR topography instrument we designed to measure the concentration changes of each hemoglobin species and map cerebral cortex functional activation. The probe of instrument consists of 4 LEDs operating at three-wavelength (735 nm & 805 nm & 850 nm) surrounded with 10 photodiodes mounted on a flexible PCB with the interoptode distance up to 2.88cm. On the basis of the modified Beer-Lambert law, the ratios of optical density changes in a ftilly oxygenated and deoxygenated state are determined by varying blood volume and hemoglobin oxygenation state in model experiment. The average ratios are 0.56. 1.74 and 0.45. Additionally, study on the phantoms is carried out to investigate the penetration depth (13 mm) of the sensor array.

Issues related to food authenticity, traceability, and fraud have increased in recent decades as a consequence of the deliberate and intentional substitution, addition, tampering, or misrepresentation of food ingredients, where false or misleading statements are made about a product for economic gains. This study aimed to evaluate the ability of a portable NIR instrument to classify egg samples sourced from different provenances or production systems (e.g., cage and free-range) in Australia. Whole egg samples (n: 100) were purchased from local supermarkets where the label in each of the packages was used as identification of the layers’ feeding system as per the Australian legislation and standards. The spectra of the albumin and yolk were collected using a portable NIR spectrophotometer (950–1600 nm). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data. The results obtained in this study showed how the combination of chemometrics and NIR spectroscopy allowed for the classification of egg albumin and yolk samples according to the system of production (cage and free range). The proposed method is simple, fast, environmentally friendly and avoids laborious sample pre-treatment, and is expected to become an alternative to commonly used techniques for egg quality assessment.

A portable IoT NIR spectroscopic system to analyze the quality of dairy farm forage

Food security and the agriculture sustainable development are two important challenges that arise in the Common Agricultural Policy (CAP) of the European Union. However, oftentimes, the necessary equipment to fully these requirements is expensive and complex to use. In this paper, a relatively low-cost prototype instrument is presented for the detection of contaminants and pathogens in forages, using near infrared spectrometry (NIRS) techniques. Through the Internet of Things (IoT) tools, data are sent to the cloud for processing while being accessible for any device. All this opens up new possibilities, and new challenges, for the sustainable management of natural resources.

Automatic cancer discrimination based on near-infrared spectrum and class-modeling technique

Portable near-infrared spectroscopy: A rapid and accurate blood test for diagnosis of Haemonchus contortus infection and for targeted selective treatment of sheep

Determination of pitaya quality using portable NIR spectroscopy and innovative low-cost electronic nose

ABSTRACTNear‐Infrared (NIR) Spectroscopy has become a pivotal non‐destructive technology in agricultural production and fruit quality assessment, particularly in the pear industry. This review comprehensively examines recent advancements in NIR Spectroscopy for evaluating critical quality parameters such as Soluble Solids Content (SSC), firmness, and moisture content, which are essential for determining optimal harvest timing, improving storage practices, and extending shelf life. The evolution of portable NIR spectrometers is explored, emphasizing their increasing suitability for real‐time, in‐field applications while addressing challenges such as calibration variability and environmental factors. Additionally, the integration of NIR spectroscopy with imaging technologies like Hyperspectral Imaging (HSI) and Multispectral Imaging (MSI) is highlighted for their potential to enhance the precision and scope of fruit quality assessment. The review further considers incorporating low‐cost technologies, such as electronic noses and machine vision, with NIR spectroscopy to create cost‐effective and robust multi‐sensor platforms. By synthesizing these developments, the review identifies gaps in current methodologies, such as limited model generalization across diverse pear varieties, and proposes solutions like data fusion and advanced machine learning approaches. These strategies are poised to improve model adaptability and accuracy, ensuring NIR spectroscopy remains at the forefront of pear quality assessment. Future research directions emphasize the need for integrating cutting‐edge technologies with NIR spectroscopy to achieve more sustainable and efficient agricultural practices.

Irrigation decision support based on leaf relative water content determination in olive grove using near infrared spectroscopy

Recent research has shown the potential of portable and handheld NIR instruments to monitor and measure the composition of fruits and vegetables. Current research has also shown the possibility of using portable instruments as tools to monitor composition along the entire food value chain. The objective of this study was to evaluate two sample presentation methods (dry powder and fruit puree) to measure total soluble solids (TSS) and moisture (M) in wild harvested Kakadu plum (KP) ( Terminalia ferdinandiana, Combretaceae). Kakadu plum is an endemic plant of Australia that contains high concentrations of vitamin C, ellagic acid as well as other bioactive compounds. These properties make this plant of high economic and social importance for the Aboriginal communities of Australia. Fruit samples were wild harvested in January 2020 from locations in the Kimberley region (Western Australia, Australia) and analysed using both reference and NIR spectroscopic methods. The SECV and RPD values in cross validation were 0.65% (RPD: 2.2) and 0.22% (RPD: 4.2) to predict M and TSS in the KP dry powder samples. The SECV and RPD values obtained in cross validation for the KP fruit puree samples were 0.56% (RPD: 2.8) and 0.24% (RPD: 3.8) for M and TSS, respectively. The results of this study demonstrated the ability of NIR spectroscopy to measure M and TSS in wild harvest fruit. These findings can be also utilised by the Aboriginal communities to develop a grading/sorting system to rapidly screen and evaluate relevant chemical parameters associated with fruit quality and safety.

Dendrobium officinale is a valuable medicinal and edible homologous health food. It has immunomodulatory, antioxidant, and metabolism-regulating properties. However, its adulteration is widespread, seriously compromising product quality and safety. Traditional adulteration detection methods are complex, costly, and time-consuming, making it urgent to establish a rapid and non-destructive detection approach. This study developed a rapid identification and quantification method for adulterated D. officinale. The method combined near-infrared (NIR) spectroscopy with data-driven soft independent modeling of class analogy (DD-SIMCA) and partial least squares regression (PLSR) models. PCA, PLS-DA, and OPLS-DA were first used to visualize sample clustering and group differences. DT, SVM, ANN, and NB were used for classification. DD-SIMCA and PLSR were used for one-class modeling and quantitative analysis. Raw spectral data were preprocessed using multiplicative scatter correction (MSC), the standard normal variate (SNV), the first derivative, and Savitzky–Golay smoothing. In the identification analysis, the DD-SIMCA model achieved 100% sensitivity and 100% specificity in the validation set. Its overall accuracy in the independent test set was 99.2%, demonstrating excellent discrimination performance. In addition, SVM combined with NIR also achieved good accuracy. In the quantitative analysis of adulteration, the PLSR model predicted different adulteration levels. Most calibration and validation sets showed R2 values above 0.99 and RMSE values below 0.05, indicating excellent predictive performance. The results indicate that NIR combined with DD-SIMCA and PLSR can achieve rapid identification and accurate quantification of adulterated D. officinale samples. This approach provides strong support for quality control and regulatory supervision of high-value health foods.

Comparison of wavelength selected methods for improving of prediction performance of PLS model to determine aflatoxin B1 (AFB1) in wheat samples during storage

Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples (n = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey.