Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology

To explore the application of near-infrared (NIR) technology to the quality analysis of raw intact tobacco leaves, a nondestructive discrimination method based on NIR spectroscopy is proposed. A “multiregion + multipoint” NIR spectrum acquisition method is developed, allowing 18 NIR diffuse reflectance spectra to be collected from an intact tobacco leaf. The spectral characteristics and spectral preprocessing methods of intact tobacco leaves are analyzed, and then different spectra (independent or average spectra) and different algorithms (discriminant partial least-squares (DPLS) and Fisher’s discriminant algorithms) are used to construct discriminant models for verifying the feasibility of intact leaf modeling and determining the optimal model conditions. Qualitative discrimination models based on the position, green-variegated (GV), and the grade of intact tobacco leaves are then constructed using the NIR spectra. In the application and verification stage, a multiclassification voting mechanism is used to fuse the results of multiple spectra from a single tobacco leaf to obtain the final discrimination result for that leaf. The results show that the position-GV discrimination model constructed using independent spectra and the DPLS algorithm and the grade discrimination model constructed using independent spectra and Fisher’s algorithm achieve optimal results with intact leaf NIR wavenumbers from 5006–8988 cm−1 and the first-derivative and standard normal variate transformation preprocessing method. Finally, when applied to new tobacco leaves, the position-GV model and the grade model achieve discrimination accuracies of 95.18% and 92.77%, respectively. This demonstrates that the two models have satisfactory qualitative discrimination ability for intact tobacco leaves. This study has established a feasible method for the nondestructive qualitative discrimination of the position, GV, and grade of intact tobacco leaves based on NIR technology.

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Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Journal of Analytical Methods in Chemistry ◽

10.1155/2021/9912589 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yi Chen ◽

Jun Bin ◽

Congming Zou ◽

Mengjiao Ding

Keyword(s):

Deep Learning ◽

Near Infrared ◽

Nir Spectroscopy ◽

Learning Ability ◽

Support Vector ◽

Maturity Level ◽

Promising Alternative ◽

Tobacco Leaves ◽

Tobacco Leaf ◽

Leaf Maturity

The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches—K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)—were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.

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Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy

Australian Journal of Experimental Agriculture ◽

10.1071/ea9910205 ◽

1991 ◽

Vol 31 (2) ◽

pp. 205 ◽

Cited By ~ 27

Author(s):

KF Smith ◽

PC Flinn

Keyword(s):

Nutritive Value ◽

Near Infrared ◽

Linear Regression Analysis ◽

Spectral Characteristics ◽

Nir Spectroscopy ◽

Cost Effective ◽

Multiple Linear Regression Analysis ◽

Infrared Reflectance ◽

Near Infrared Reflectance

Near infrared reflectance (NIR) spectroscopy is a rapid and cost-effective method for the measurement of organic constituents of agricultural products. NIR is widely used to measure feed quality around the world and is gaining acceptance in Australia. This study describes the development of an NIR calibration to measure crude protein (CP), predicted in vivo dry matter digestibility (IVDMD) and neutral detergent fibre (NDF) in temperate pasture species grown in south-western Victoria. A subset of 116 samples was selected on the basis of spectral characteristics from 461 pasture samples grown in 1987-89. Several grass and legume species were present in the population. Stepwise multiple linear regression analysis was used on the 116 samples to develop calibration equations with standard errors of 0.8,2.3 and 2.2% for CP, NDF and IVDMD, respectively. When these equations were tested on 2 independent pasture populations, a significant bias existed between NIR and reference values for 2 constituents in each population, indicating that the calibration samples did not adequately represent the new populations for these constituents. The results also showed that the H statistic alone was inadequate as an indicator of equation performance. It was confirmed that it was possible to develop a broad-based calibration to measure accurately the nutritive value of closed populations of temperate pasture species. For the resulting equations to be used for analysis of other populations, however, they must be monitored by comparing reference and NIR analyses on a small number of samples to check for the presence of bias or a significant increase in unexplained error.

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Near infrared spectroscopy for rapid determination of solids content of amino resins

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520947824 ◽

2020 ◽

Vol 28 (5-6) ◽

pp. 344-350

Author(s):

M Gonçalves ◽

NT Paiva ◽

JM Ferra ◽

J Martins ◽

F Magalhães ◽

...

Keyword(s):

Near Infrared ◽

Rapid Determination ◽

Nir Spectroscopy ◽

Coefficient Of Determination ◽

Water Addition ◽

Reliable Technique ◽

Standard Normal Variate ◽

And Performance ◽

Solids Content

Near infrared (NIR) spectroscopy is a fast and reliable technique for assessing properties of amino resins. One important property that defines the cost and performance of these resins is the solids content (SC). This work studied the prediction of SC of amino resins by combining NIR spectroscopy with partial least squares (PLS) regression. A total of 990 industrial NIR spectra of amino resins were obtained and split randomly by a ratio of 2/3 for calibration and 1/3 for validation. The best model achieved a root mean-square error of prediction (RMSEP) of 0.32% (m/m) and a coefficient of determination of prediction ([Formula: see text]) of 81%. standard normal variate (SNV) was found to be the NIR pre-processing that provided the best results for model construction. Addition of water to two amino resins showed that the NIR model does not respond to the water addition, despite water making great contribution to the SC value. An inference that can be obtained from this is that the NIR model of amino resins uses NIR properties of amino resins that relate to the SC and from there predict the most probable SC, instead of looking at all the components that affect the SC of amino resins.

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Rapid Discrimination of the Geographical Origins of an Oolong Tea (Anxi-Tieguanyin) by Near-Infrared Spectroscopy and Partial Least Squares Discriminant Analysis

Journal of Analytical Methods in Chemistry ◽

10.1155/2014/704971 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 16

Author(s):

Si-Min Yan ◽

Jun-Ping Liu ◽

Lu Xu ◽

Xian-Shu Fu ◽

Hai-Feng Cui ◽

...

Keyword(s):

Discriminant Analysis ◽

Least Squares ◽

Partial Least Squares ◽

Sensitivity And Specificity ◽

Near Infrared ◽

Nir Spectroscopy ◽

Classification Model ◽

Standard Normal Variate ◽

Statistical Model Selection ◽

Rapid Discrimination

This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV) transformation, taking second-order derivatives (D2) spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

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Identification of geographical origin of TPFD based on handheld NIR spectroscopy and PLSDA

NIR news ◽

10.1177/0960336020944007 ◽

2020 ◽

Vol 31 (5-6) ◽

pp. 25-29

Author(s):

Rita-Cindy Aye-Ayire Sedjoah ◽

Bangxing Han ◽

Hui Yan

Keyword(s):

Quality Control ◽

Near Infrared ◽

Geographical Origin ◽

Nir Spectroscopy ◽

Analysis Model ◽

First Order ◽

Standard Normal Variate ◽

Standard Normal ◽

Infrared Spectrometer ◽

Near Infrared Spectrometer

The present study is focused on the identification of geographical origin (Zhejiang, Yunnan and Anhui, China) of Dendrobium officinale’s dried stem called Tiepi fengdou by mean of the handheld near-infrared spectrometer. Raw data were preprocessed to reduce unwanted spectral variations by the first-order derivative followed by standard normal variate transformation, and partial least squares discriminant analysis model was developed for calibration. The results showed that more than 90% of the origins were identified. Therefore, it is possible to classify the geographical origin of Tiepi fengdou by the use of the handheld near-infrared spectrometer for effective quality control.

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A Preliminary Study on the near Infrared Spectral Characteristics of Purchased Flue-Cured Tobacco

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.396-398.2027 ◽

2011 ◽

Vol 396-398 ◽

pp. 2027-2032

Author(s):

Ying Zhang ◽

Li Yuan He

Keyword(s):

Spectral Data ◽

Near Infrared ◽

Wave Length ◽

Spectral Characteristics ◽

Spectral Difference ◽

Tobacco Leaf ◽

Positive Group ◽

Grade Classification ◽

Infrared Spectral ◽

Insignificant Difference

In order to explore the application of near-infrared spectrum technology in the grading of purchased flue-cured tobacco, positive group of flue-cured tobacco with different grades was gathered; the near-infrared spectral data across different grades, in the same grade, and different test zones of the same tobacco leaf were determined and acquired to analyze their near-infrared spectral characteristics and their representativeness. The findings indicated that the positive group of flue-cured tobacco was highly homogeneous; there was identical near-infrared spectral characteristics in any zone of the same tobacco leaf; the peaks and troughs of wave length of the near-infrared spectral characteristics of different samples with identical grade were also consistent, with extremely insignificant difference in reflectivity; the near-infrared spectral difference between the tobacco leaves with different grades was much more significant than that in different test zones of the same tobacco leaf. It was highly probable to take the reflectance ratio of the peaks and troughs of wave length as the standard for grade classification. It is feasible to appraise the quality of purchased flue-cured tobacco using near-infrared spectral data.

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Automatic and Rapid Discrimination of Cotton Genotypes by Near Infrared Spectroscopy and Chemometrics

Journal of Analytical Methods in Chemistry ◽

10.1155/2012/793468 ◽

2012 ◽

Vol 2012 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Hai-Feng Cui ◽

Zi-Hong Ye ◽

Lu Xu ◽

Xian-Shu Fu ◽

Cui-Wen Fan ◽

...

Keyword(s):

Discriminant Analysis ◽

Classification Accuracy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Standard Normal Variate ◽

Pattern Recognition Methods ◽

Standard Normal ◽

Cotton Genotypes ◽

Rapid Discrimination

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n=120) and leaves (n=123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

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Agronomic characteristics of annual Trifolium legumes and nutritive values as predicted by near-infrared reflectance (NIR) spectroscopy

Crop and Pasture Science ◽

10.1071/cp10158 ◽

2011 ◽

Vol 62 (12) ◽

pp. 1078 ◽

Cited By ~ 3

Author(s):

X. Li ◽

R. L. Ison ◽

R. C. Kellaway ◽

C. Stimson ◽

G. Annison ◽

...

Keyword(s):

Organic Matter ◽

Near Infrared ◽

Spectral Characteristics ◽

Nir Spectroscopy ◽

Subterranean Clover ◽

Trifolium Subterraneum ◽

Infrared Reflectance ◽

Near Infrared Reflectance ◽

Stem Leaf

A range of annual legume genotypes comprising one line of Trifolium subterraneum, four lines of T. michelianum, 11 of T. resupinatum var. resupinatum, and one line of T. resupinatum var. majus were grown in glasshouses under temperature regimes of 10−15°C and 16−21°C. Dry matter (DM) weights of stem, leaf, and flower tissues were measured when plants had six nodes, at first flower appearance, and at senescence. All samples were scanned by near-infrared reflectance spectroscopy (NIRS). One-third of the samples, covering the range of spectral characteristics, were analysed for in vitro digestible organic matter (DOMD), organic matter, crude protein (CP), neutral detergent fibre (NDF), lignin, cellulose, and the hemicellulosic polysaccharide monomers arabinose, xylose, mannose, galactose, and rhamnose. These data were used to develop calibration equations from which the composition of the remaining samples was predicted by NIRS. The higher temperature resulted in plants reaching respective phenological stages earlier, but did not affect either DM yields of total plant, stem, leaf, and petiole tissues or the proportions of each fraction. In vitro DOMD and arabinose and galactose levels decreased, while lignin, cellulose, NDF, xylose, mannose, and rhamnose levels increased with advancing maturity. In vitro DOMD was positively associated with contents of CP, arabinose, galactose, and the arabinose/xylose ratio and was negatively associated with contents of lignin, cellulose, NDF, xylose, mannose, and rhamnose. Lignin contents were highly correlated with levels of both xylose and mannose. Stems were more digestible than leaves in subterranean clover and T. resupinatum var. majus. The study also demonstrated that NIRS can be used routinely as a quick, inexpensive, and reliable laboratory technique to predict feed components of annual Trifolium legumes.

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Interactions of Linearly Polarized and Unpolarized Light on Kiwifruit Using Aquaphotomics

Molecules ◽

10.3390/molecules27020494 ◽

2022 ◽

Vol 27 (2) ◽

pp. 494

Author(s):

Damenraj Rajkumar ◽

Rainer Künnemeyer ◽

Harpreet Kaur ◽

Jevon Longdell ◽

Andrew McGlone

Keyword(s):

Near Infrared ◽

Polarized Light ◽

Bound Water ◽

Free Water ◽

Nir Spectroscopy ◽

Soluble Solids ◽

Unpolarized Light ◽

Standard Normal Variate ◽

Linearly Polarized ◽

Solids Content

Near infrared (NIR) spectroscopy is an important tool for predicting the internal qualities of fruits. Using aquaphotomics, spectral changes between linearly polarized and unpolarized light were assessed on 200 commercially grown yellow-fleshed kiwifruit (Actinidia chinensis var. chinensis ‘Zesy002’). Measurements were performed on different configurations of unpeeled (intact) and peeled (cut) kiwifruit using a commercial handheld NIR instrument. Absorbance after applying standard normal variate (SNV) and second derivative Savitzky–Golay filters produced different spectral features for all configurations. An aquagram depicting all configurations suggests that linearly polarized light activated more free water states and unpolarized light activated more bound water states. At depth (≥1 mm), after several scattering events, all radiation is expected to be fully depolarized and interactions for incident polarized or unpolarized light will be similar, so any observed differences are attributable to the surface layers of the fruit. Aquagrams generated in terms of the fruit soluble solids content (SSC) were similar for all configurations, suggesting the SSC in fruit is not a contributing factor here.

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Nondestructive Determination of Diastase Activity of Honey Based on Visible and Near-Infrared Spectroscopy

Molecules ◽

10.3390/molecules24071244 ◽

2019 ◽

Vol 24 (7) ◽

pp. 1244

Author(s):

Zhenxiong Huang ◽

Lang Liu ◽

Guojian Li ◽

Hong Li ◽

Dapeng Ye ◽

...

Keyword(s):

Least Squares ◽

High Temperatures ◽

Near Infrared ◽

Spectral Characteristics ◽

Nir Spectroscopy ◽

Kinetic Mechanism ◽

National Standard ◽

Support Vector ◽

Heating Process

The activities of enzymes are the basis of evaluating the quality of honey. Beekeepers usually use concentrators to process natural honey into concentrated honey by concentrating it under high temperatures. Active enzymes are very sensitive to high temperatures and will lose their activity when they exceed a certain temperature. The objective of this work is to study the kinetic mechanism of the temperature effect on diastase activity and to develop a nondestructive approach for quick determination of the diastase activity of honey through a heating process based on visible and near-infrared (Vis/NIR) spectroscopy. A total of 110 samples, including three species of botanical origin, were used for this study. To explore the kinetic mechanism of diastase activity under high temperatures, the honey of three kinds of botanical origins were processed with thermal treatment to obtain a variety of diastase activity. Diastase activity represented with diastase number (DN) was measured according to the national standard method. The results showed that the diastase activity decreased with the increase of temperature and heating time, and the sensitivity of acacia and longan to temperature was higher than linen. The optimum temperature for production and processing is 60 °C. Unsupervised clustering analysis was adopted to detect spectral characteristics of these honeys, indicating that different botanical origins of honeys can be distinguished in principal component spaces. Partial least squares (PLS) and least squares-support vector machine (LS-SVM) algorithms were applied to develop quantitative relationships between Vis/NIR spectroscopy and diastase activity. The best result was obtained through Gaussian filter smoothing-standard normal variate (GF-SNV) pretreatment and the LS-SVM model, known as GF-SNV-LS-SVM, with a determination coefficient (R2) of prediction of 0.8872, and root mean square error (RMSE) of prediction of 0.2129. The overall results of this paper showed that the diastase activity of honey can be determined quickly and non-destructively with Vis/NIR spectral methods, which can be used to detect DN in the process of honey production and processing, and to maximize the nutrient content of honey.

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