Light at the museum – A near impossible result

The monitoring and quantification of the illegal harvest of protected animal products is very vital for the conservation and protection of endangered species. Most of the methods and techniques used in the trade of these products are recognised to be incredibly time consuming and labour intensive requiring significant analyst expertise. In this study, we have demonstrated the potential of near-infrared spectroscopy combined with either principal component analysis or partial least square discriminant analysis regression as a rapid and non-invasive tool to classify horn and ivory samples stored in the Australian Museum, Sydney. This study has also demonstrated the attractiveness of the near-infrared technique as a screening tool that could revolutionise the tracking and identification of contraband materials produced from horn and ivory biomaterials.

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A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520987315 ◽

2021 ◽

pp. 096703352098731

Author(s):

Adenilton C da Silva ◽

Lívia PD Ribeiro ◽

Ruth MB Vidal ◽

Wladiana O Matos ◽

Gisele S Lopes

Keyword(s):

Quality Control ◽

Discriminant Analysis ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Chemical Components ◽

Linear Discriminant ◽

Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

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Prediction of macro- and microelements content in Croatian common bean landraces (Phaseolus vulgaris L.) by NIR spectroscopy

Poljoprivreda ◽

10.18047/poljo.25.1.7 ◽

2019 ◽

Vol 25 (1) ◽

pp. 48-55

Author(s):

Marina Vranić ◽

Marko Petek ◽

Krešimir Bošnjak ◽

Boris Lazarević ◽

Klaudija Carović Stanko

Keyword(s):

Phaseolus Vulgaris ◽

Common Bean ◽

Near Infrared ◽

Model Development ◽

Nir Spectroscopy ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Potassium Oxide ◽

Phaseolus Vulgaris L

In this study, near-infrared spectroscopy (NIRS) was used to predict the contents of essential macro- and microelements in common bean (Phaseolus vulgaris L.) accessions of most widespread Croatian landraces. Total of 175 samples were used for the model development by modified partial least square (MPLS), principal component regression (PCR) and partial least square (PLS) techniques. Based on the coefﬁcients of determination (R2), standard error of calibration (SEC) and error of prediction (SEP) the models developed were (i) nearly applicable for nitrogen (N) (0.89, 0.12 and 0.45 respectively), (ii) poor for iron (Fe), cinc (Zn), potassium oxide (K2O) and potassium (K), (iii) usable for phosphorus pentoxide (P2O5), phosphorus (P), phytic acid (PA) and manganese (Mn). The MPLS regression statistics suggested the most accurate models developed comparing with PLS and PCR. It was concluded that a wider set of common bean samples needs to be used for macro- and microelements prediction by NIRS.

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Prediction of Soil Texture Distributions by using PLSR and Reflectance Spectroscopy

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6558.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 7876-7881

Keyword(s):

Statistical Analysis ◽

Soil Texture ◽

Near Infrared ◽

Diffuse Reflectance Spectroscopy ◽

Principal Component ◽

Reflectance Spectroscopy ◽

Soil Samples ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression

The texture of soil i.e. Sand, Silt and Clay are the most important physical properties of soil for agricultural management. In the agricultural practices to increase the productivity of soil, moisture-holding capacity, aeration and to support the agronomic decisions the knowledge of soil texture is an essential task. For this purpose, the present research gives better results and fast acquisition of soil information with the use of Visible and Near Infrared (Vis- NIR) Diffuse Reflectance Spectroscopy. A total of 30 soil samples from two different locations from Aurangabad, Maharashtra, India were collected and analyzed for soil texture. To detect the soil texture the Vis-NIR DRS has shown levels of accurate results compared to the traditional laboratory method with less time, cost and effort. To measure the reflectance of soil the ASD FieldSpec4 Spectroradiometer (350-2500nm) was used. By the observation of captured spectra by using Spectroradiometer it showed that on the basis of different textural classes the soil samples could be spectrally separable. For database collection and pre-processing, we have used RS3 and ViewSpec Pro software respectively. The statistical analysis by using the combination of Principal Component Analysis (PCA) and Partial Least Square Regression method gives accurate results. To determine the texture of soil sample thirteen features were calculated. The main goal of this research was to determine the soil texture by using statistical methods and to test the performance of VNIR-SWIR reflectance spectroscopy by using the ASD FieldSpec4 Spectroradiometer for estimation of the texture of the soil. The results showed that R2 = 0.99 gives maximum accuracy for clay content and R2 = 0.988 for silt content and R2 = 0.989 for sand. The Root Mean Square Values (RMSE) for clay, silt, and sand are 0.02392, 0.02399 and 0.02289 respectively. With the use of reflectance spectroscopy and statistical analysis by using regression models we can determine the soil properties accurately in very less time.

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Spectroscopic and Molecular Methods to Differentiate Gender in Immature Date Palm (Phoenix dactylifera L.)

Plants ◽

10.3390/plants10030536 ◽

2021 ◽

Vol 10 (3) ◽

pp. 536

Author(s):

Abdul Latif Khan ◽

Ahmed Al-Harrasi ◽

Muhammad Numan ◽

Noor Mazin AbdulKareem ◽

Fazal Mabood ◽

...

Keyword(s):

Date Palm ◽

Near Infrared ◽

Sex Differentiation ◽

Principal Component ◽

Phoenix Dactylifera ◽

Partial Least Square ◽

Least Square ◽

Transcript Accumulation ◽

Near Infrared Reflectance ◽

Phoenix Dactylifera L

Phoenix dactylifera (date palm) is a well-known nutritious and economically important fruit tree found in arid regions of the Middle East and North Africa. Being diploid, it has extremely high divergence in gender, where sex differentiation in immature date palms (Phoenix dactylifera L.) has remained an enigma in recent years. Herein, new robust infrared (near-infrared reflectance spectroscopy (NIRS) and Fourier transform infrared attenuated total reflectance (FTIR/ATR)) and nuclear magnetic resonance (NMR) spectroscopy methods coupled with extensive chemometric analysis were used to identify the sex differentiation in immature date palm leaves. NIRS/FTIR reflectance and 1H-NMR profiling suggested that the signals of monosaccharides (glucose and fructose) and/or disaccharides (maltose and sucrose) play key roles in sex differentiation. The three kinds of spectroscopic data were clearly differentiated among known and unknown male and female leaves via principal component and partial least square discriminant analyses. Furthermore, sex-specific genes and molecular markers obtained from the lower halves of LG12 chromosomes showed enhanced transcript accumulation of mPdIRDP52, mPdIRDP50, and PDK101 in females compared with in males. The phylogeny showed that the mPdIRD033, mPdIRD031, and mPdCIR032 markers formed distinctive clades with more than 70% similarity in gender differentiation. The three robust analyses provide an alternative tool to differentiate sex in date palm trees, which offers a solution to the long-standing challenge of dioecism and could enhance in situ tree propagation programs.

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Non-Invasive Blood Glucose Estimation using Handheld Near Infra-Red Device

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c1004.1083s19 ◽

2019 ◽

Vol 8 (3S) ◽

pp. 16-19 ◽

Cited By ~ 2

Keyword(s):

Blood Glucose ◽

Near Infrared ◽

Blood Glucose Measurement ◽

Partial Least Square ◽

Least Square ◽

Blood Glucose Monitoring ◽

Glucose Measurement ◽

Coefficient Of Determination ◽

Diabetic Patients ◽

Non Invasive

Non-invasive blood glucose measurement would ease everyday life of diabetic patients and may cut the cost involved in their treatments. This project aims at developing a non-invasive blood glucose measurement using NIR (near infrared) spectroscopic device. NIR spectra data and blood glucose levels were collected from 45 participants, resulting 90 samples (75 samples for calibration and 15 samples for testing) in this project. These samples were then used to develop a predictive model using Interval Partial Least Square (IPLS) regression method. The results obtained from this project indicate that the handheld micro NIR has potential use for rapid non-invasive blood glucose monitoring. The coefficient of determination (R 2 ) obtained for calibration/training and testing dataset are respectively 0.9 and 0.91.

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Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging

Foods ◽

10.3390/foods9020154 ◽

2020 ◽

Vol 9 (2) ◽

pp. 154 ◽

Cited By ~ 13

Author(s):

Hongzhe Jiang ◽

Fengna Cheng ◽

Minghong Shi

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Limit Of Detection ◽

Principal Component ◽

Rapid Identification ◽

Performance Comparison ◽

Correlation Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression

Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

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Rapid Assessment of Exercise State through Athlete’s Urine Using Temperature-Dependent NIRS Technology

Journal of Analytical Methods in Chemistry ◽

10.1155/2020/8828213 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Lihe Ding ◽

Lei-ming Yuan ◽

Yiye Sun ◽

Xia Zhang ◽

Jianpeng Li ◽

...

Keyword(s):

Physical Function ◽

Near Infrared ◽

Rapid Assessment ◽

Nir Spectroscopy ◽

Principal Component ◽

Partial Least Square ◽

Nutritional Supplementation ◽

Least Square ◽

Basketball Player ◽

Temperature Dependent

Athletes usually take nutritional supplements and perform the specialized training to improve the performance of sport. A quick assessment of their athletic status will help to understand the current physical function of athletes’ status and the effect of nutritional supplementation. Human urine, as one of the most important body indicators, is composed of many metabolites, which can provide effective monitoring information for physical conditions. In this study, temperature-dependent near-infrared spectroscopy (NIRS) technology was used to collect the spectra of athlete’s urine for evaluating the feasibility of rapidly detecting the exercise state of the basketball player. To obtain the detection results accurately, several chemometrics methods including principal component analysis (PCA), variables selection method of variable importance in projection (VIP), continuous 1D wavelet transform (CWT), and partial least square-discriminant analysis (PLS-DA) were employed to develop a classifier to distinguish the physical status of athletes. The optimal classifying results were obtained by wavelet-PLS-DA classifier, whose average precision, sensitivity, and specificity are all above 0.95, and the overall accuracy of all samples is 0.97. These results demonstrate that temperature-dependent NIRS can be used to rapidly assess the physical function of athlete’s status and the effect of nutritional supplementation is feasible. It can be believed that temperature-dependent NIR spectroscopy will obtain applications more widely in the future.

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Characterization of green, roasted beans, and ground coffee using near infrared spectroscopy: A comparison of two devices

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033519825665 ◽

2019 ◽

Vol 27 (1) ◽

pp. 93-104 ◽

Cited By ~ 3

Author(s):

Alessio Tugnolo ◽

Roberto Beghi ◽

Valentina Giovenzana ◽

Riccardo Guidetti

Keyword(s):

Real Time ◽

Near Infrared ◽

Principal Component ◽

Quality Parameters ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Process Measurement ◽

Tap Density ◽

Ground Coffee

The aim of this work was to compare two near infrared spectrometers (960–1650 nm) to assess coffee matrices for real-time characterization during processing. A benchtop spectrophotometer built for process measurement, equipped with a rotating acquisition system suitable for in-line analyses (device 1), and a portable spectrophotometer for at-line analyses (device 2) were tested. The experimentation was conducted on green, roasted bean, and ground coffee, relating to three coffee blends, employing samples collected at different steps from the process. A total of 399 (247 green, 76 roasted, and 76 ground coffee samples) and 229 (142 green, 43 roasted and 44 ground coffee samples) samples were analyzed using device 1 and device 2, respectively. Principal component analysis was applied to the spectra of the coffee samples to evaluate the feasibility of real-time characterization for each matrix type, during the different steps of the process. Partial least square regression analysis was applied to develop calibrations to predict moisture content, tap density, and powder granulometry on ground coffee and to estimate moisture on roasted bean with a view of real-time measurements. The Passing and Bablok regression method and Bland–Altman analysis were performed to compare results obtained from the tested devices. Considering the models built on different datasets were based on different blends, quality parameters, and matrices, the results obtained from partial least square models, in cross-validation, gave R2 values between 0.21 and 0.98 and between 0.18 and 0.84 for devices 1 and 2, respectively. However, considering the comparison of model results derived from the two devices, no significant differences are noticeable for the most part of the dataset analyzed. Hence, based on the strategy to be undertaken by coffee industry operators, a future real-scale application could be envisaged directly in-line using device 1 or at-line using device 2.

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Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products

Sensors ◽

10.3390/s20185322 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5322

Author(s):

Hongyan Zhu ◽

Aoife Gowen ◽

Hailin Feng ◽

Keping Yu ◽

Jun-Li Xu

Keyword(s):

Near Infrared ◽

Principal Component ◽

Food Products ◽

Partial Least Square ◽

Least Square ◽

Support Vector ◽

White Stripe ◽

Practical Applications ◽

Spatial Features

Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe (“myocommata”) and red muscle (“myotome”) pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.

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Rapid Classification Of Agricultural Products Based On Their Electro-Optic Properties Using Near Infrared Reflectance And Chemometrics

Rona Teknik Pertanian ◽

10.17969/rtp.v8i1.2683 ◽

2015 ◽

Vol 8 (1) ◽

pp. 10-18

Author(s):

Agus A. Munawar

Keyword(s):

Near Infrared ◽

Scatter Correction ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Agricultural Products ◽

Near Infrared Reflectance ◽

Electro Optic ◽

Non Destructive

Abstract. Near infrared technology have been widely applied in many fields, including agriculture especially in sorting and grading process. The advantage of this technology: simple sample preparation, rapid, effective and non-destructive. The main objective of this study is to evaluate the feasibility of NIR technology in classifying several agricultural products based on their electro-optic properties. NIR diffuse reflectance spectra of apples, bananas, mangoes, garlics, tomatoes, green grapes, red grapes and oranges were acquired in wavelength range of 1000-2500 nm with gradual increment of 2 nm. Chemometrics methods were applied in combination with NIR spectra data. Classification was performed by applying principal component analysis (PCA) followed by non-iterative partial least square (NIPALS) cross validation. The results showed that NIR and chemometrics was able to differentiate and classify these agricultural products with two latent variables (2 PCs) and total explained variance of 97% (88% PC1 and 9% PC2). Furthermore, it also showed that multiplicative scatter correction (MSC) was found to be effective spectra correction or enhancement method and increased classification accuracy and robustness. It may conclude that NIR technology combined with chemometrics was feasible to apply as a rapid and non-destructive method for sorting and grading agricultural products. Rapid Classification Of Agricultural Products Based On Their Electro-Optic Properties Using Near Infrared Reflectance And ChemometricsAbstract. Aplikasi teknologi near infra red (NIR) telah digunakan dalam banyak bidang, termasuk untuk bidang pertanian terutama pada proses sortasi dan grading. Keunggulan metode ini antara lain : rapid, efektif, simultan dan tanpa merusak objek yang dikaji. Tujuan utama dari studi ini adalah untuk mengkaji potensi NIR dalam mengklasifikasi beberapa produk pertanian berdasarkan karakteristik sifat elektro-optik dari produk tersebut. Spektrum NIR pada panjang gelombang 1000 – 2500 nm dengan increment 2 nm diakuisisi untuk produk pertanian : apel, pisang, manga, bawang putih, tomat, anggur hijau, anggur merah dan jeruk. Metode chemo metrics digunakan dalam studi ini untuk dikombinasikan dengan spektrum NIR. Klasifikasi produk pertanian dilakukan dengan menerapkan metode principal component analysis (PCA) yang disertai dengan metode non-iterative partial least square (NIPALS) cross validation. Hasil studi menunjukkan bahwa kombinasi NIR dan chemo metrics mampu membedakan dan mengklasifikasi produk pertanian tersebut dengan menggunakan dua latent variable pada PCA (2 PCs) dengan total explained variance 97% (88% PC1 dan 9% PC2). Selain itu, dari studi ini juga didapatkan bahwa perbaikan data spectrum dengan metode multiplicative scatter correction (MSC) sebelum klasifikasi mampu meningkatkan akurasi hasil klasifikasi. Secara umum, dapat disimpulkan bahwa teknologi NIR dan chemo metrics dapat dijadikan sebagai metode yang efektif untuk sortasi dan atau grading produk pertanian.

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