scholarly journals Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy

2019 ◽  
Vol 9 (23) ◽  
pp. 5058 ◽  
Author(s):  
Zeng ◽  
Lü ◽  
Qiu ◽  
Lu ◽  
Jiang
Keyword(s):  
Near Infrared ◽  
Seed Quality ◽  
Hierarchical Classification ◽  
Nir Spectroscopy ◽  
Predictive Performance ◽  
Nondestructive Method ◽  
Maturation Stage ◽  
Seed Industry ◽  
Intrinsic Quality ◽  
Classification Strategy

The maturity of seeds at harvest determines their intrinsic quality characteristics such as longevity and vigor, and these characteristics are dominant factors for seed quality evaluation in the seed industry. However, little information is available on how to identify and further classify the maturation stage of seeds in a way that is nondestructive, precise, rapid, and inexpensive, while also exactly meeting the need for the uniform control of seed performance in the seed industry to improve crop yield. This study demonstrated a nondestructive method for detecting seed maturity by using the single-kernel near-infrared spectroscopy (SK-NIRS) technique. The results showed that five classes of cucumber seeds with different maturation levels can be distinguished successfully. A tree-structured hierarchical classification strategy consisting of one soft independent modeling of class analogy (SIMCA) model and three partial least squares discriminant analysis (PLS-DA) models were proposed ending up with 99.69% of the overall classification accuracy and 0.9961 of Cohen’s kappa in the test set, and its predictive performance was superior to both SIMCA and PLS-DA for direct multiclass classification. SK-NIRS in combination with a multiclass hierarchical classification strategy was proved to be both intuitive and efficient in classifying cucumber seeds according to maturation levels.

scholarly journals Soluble Solids and Simple Sugars Measurement in Intact Mango Using Near Infrared Spectroscopy

2008 ◽  
Vol 18 (3) ◽  
pp. 410-416 ◽  
Author(s):  
Stephen R. Delwiche ◽  
Weena Mekwatanakarn ◽  
Chien Y. Wang
Keyword(s):  
Standard Error ◽  
Quality Evaluation ◽  
Near Infrared ◽  
Wavelength Region ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Soluble Solids ◽  
Least Squares Regression ◽  
Different Temperatures ◽  
The Individual

A rapid, reliable, and nondestructive method for quality evaluation of mango (Magnifera indica) fruit is important to the mango industry for international trade. The objective of this study was to determine the potential of near-infrared (NIR) spectroscopy to predict soluble solids content (SSC) and individual and combined concentrations of sucrose, glucose, and fructose nondestructively in mango. Mature mangoes at two different temperatures (15 °C and 20 °C) were measured by NIR interactance (750–1088 nm wavelength region analyzed) over an 11-day period, starting when the fruit were underripe and extending to a few days past optimal ripeness. Partial least squares regression was used to develop models for SSC, individual sugar concentration, and the sum of the concentrations of the three sugars. Such analyses yielded calibration equations with R2 = 0.77 to 0.88 (SSC), 0.75 (sucrose), 0.67 (glucose), 0.70 (fructose), and 0.82 (sum); standard error of calibration = 0.56 to 0.90 (SSC), 10.0 (sucrose), 0.9 (glucose), 4.5 (fructose), and 10.4 (sum); and standard error of cross-validation = 0.93 to 1.10 (SSC), 15.6 (sucrose), 1.4 (glucose), 6.9 (fructose), and 16.8 (sum). When the SSC calibration was applied to a separate validation set, the standard error of performance ranged from 0.94% to 1.72%. These results suggest that for assessment of mango ripeness, NIR SSC calibrations are superior to the NIR calibrations for any of the individual sugars. This nondestructive technology can be used in the screening and grading of mangoes and in quality evaluation at wholesale and retail levels.

scholarly journals Nondestructive Discrimination of Lead (Pb) in Preserved Eggs (Pidan) by Near-Infrared Spectroscopy and Chemometrics

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lu Xu ◽  
Si-Min Yan ◽  
Chen-Bo Cai ◽  
Xiao-Ping Yu
Keyword(s):  
Least Squares ◽  
Inductively Coupled Plasma ◽  
Near Infrared ◽  
Safety Concern ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Nonlinear Least Squares ◽  
Classification Performance ◽  
Nondestructive Method ◽  
Support Vector

A major safety concern with pidan (preserved eggs) has been the usage of lead (II) oxide (PbO) during its processing. This paper develops a rapid and nondestructive method for discrimination of lead (Pb) in preserved eggs with different processing methods by near-infrared (NIR) spectroscopy and chemometrics. Ten batches of 331 unleaded eggs and six batches of 147 eggs processed with usage of PbO were collected and analyzed by NIR spectroscopy. Inductively coupled plasma mass spectrometry (ICP-MS) analysis was used as a reference method for Pb identification. The Pb contents of leaded eggs ranged from 1.2 to 12.8 ppm. Linear partial least squares discriminant analysis (PLSDA) and nonlinear least squares support vector machine (LS-SVM) were used to classify samples based on NIR spectra. Different preprocessing methods were studied to improve the classification performance. With second-order derivative spectra, PLSDA and LS-SVM obtained accurate and reliable classification of leaded and unleaded preserved eggs. The sensitivity and specificity of PLSDA were 0.975 and 1.000, respectively. Because the strictest safety standard of Pb content in traditional pidan is 2 ppm, the proposed method shows the feasibility for rapid and nondestructive discrimination of Pb in Chinese preserved eggs.

scholarly journals Assessment of pesticide coating on cereal seeds by near infrared hyperspectral imaging

2017 ◽  
Author(s):  
Ph. Vermeulen ◽  
P. Flémal ◽  
O. Pigeon ◽  
P. Dardenne ◽  
J. Fernández Pierna ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Seed Quality ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Ultra Performance Liquid Chromatography

Classical chromatographic methods, such as ultra performance liquid chromatography (UPLC), are used as reference methods to assess seed quality and homogeneous pesticide coating of seeds. These methods have some important drawbacks since they are time consuming, expensive, destructive and require a substantial amount of solvent, among others. Near infrared (NIR) spectroscopy seems to be an interesting alternative technique for the determination of the quality of seed treatment and avoids most of these drawbacks. The objective of this study was to assess the quality of pesticide coating treatment by near infrared hyperspectral imaging (NIR-HSI) by analysing, on a seed-by-seed basis, several seeds simultaneously in comparison to NIR spectroscopy and UPLC as the reference method. To achieve this goal, discrimination—partial least squares discriminant analysis (PLS-DA)—models and regression—partial least squares (PLS)—models were developed. The results obtained by NIR-HSI are compared to the results obtained with NIR spectroscopy and UPLC instruments. This study has shown the potential of NIR hyperspectral imaging to assess the quality/homogeneity of the pesticide coating on seeds.

scholarly journals Simultaneous Determination of Several Fiber Contents in Blended Fabrics by Near-Infrared Spectroscopy and Multivariate Calibration

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Full Spectrum ◽  
Lattice Design ◽  
The Mean

The qualitative and quantitative determination of the components of textile fibers takes an important position in quality control. A fast and nondestructive method of simultaneously analyzing four fiber components in blended fabrics was studied by near-infrared (NIR) spectroscopy combined with multivariate calibration. Two sample sets including 39 and 25 samples were designed by simplex mixture lattice design methods and used for experiment. Four components include wool, polyester, polyacrylonitrile, and nylon and their mixture is one of the most popular formulas of textiles. Uninformative variable elimination-partial least squares (UVEPLS) and the full-spectrum partial least squares (PLS) were used as the tool. On the test set, the mean standard error of prediction (SEP) and the mean ratio of the standard deviation of the response variable and SEP (RPD) of the full-spectrum PLS model and UVEPLS model were 0.38, 0.32 and 7.6, 8.3, respectively. This result reveals that the UVEPLS can construct local models with acceptable and better performance than the full-spectrum PLS. It indicates that this method is valuable for nondestructive analysis in the field of wool content detection since it can avoid time-consuming, costly, and laborious wet chemical analysis.

Near Infrared Reflectance Spectroscopic Determination of Protein Nitrogen in Plant Tissue

1984 ◽  
Vol 67 (3) ◽  
pp. 506-509
Author(s):  
Robert A Isaac ◽  
William C Johnson
Keyword(s):  
Plant Tissue ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Leaf Tissue ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Protein Nitrogen

Abstract A rapid, nondestructive method is described for the determination of protein nitrogen in plant tissue, using near infrared reflectance (NIR) spectroscopy. Procedures for instrument calibration are discussed. Comparisons between Kjeldahl nitrogen and NIR nitrogen are made for corn leaf tissue from Georgia and Indiana. Multiple correlation coefficients for other plant tissues such as peanuts, soybean, wheat, pecan, bermuda grass, and bent grass are also shown.

A Rapid and Nondestructive Method for the Prediction of Ibuprofen Tablet Hardness Using NIR Diffuse Reflectance Spectroscopy

2019 ◽  
Vol 15 (5) ◽  
pp. 439-446
Author(s):  
Qiushi Peng ◽  
Yi Bao ◽  
Tingyu Chen ◽  
Qianrong Peng ◽  
Min Yang
Keyword(s):  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Magnesium Stearate ◽  
Nondestructive Method ◽  
Pcr Analysis ◽  
Sodium Magnesium ◽  
Tablet Hardness

Introduction: This study aimed at developing a technology to measure the hardness of Ibuprofen (IBU) tablets and optimize the IBU formulation using Near-infrared (NIR) spectroscopy. Materials and Methods: Tablets (400 mg±5%, 10mm in diameter) consisting of IBU, microcrystalline cellulose SH-103, carboxymethyl starch sodium, magnesium stearate, silicon dioxide were formed of various hardness (2kg, 4kg, 6kg, 8kg, 10kg, 12kg). The reflectance NIR spectra of various tablets were employed to establish 9 calibrations models, which were further used to predict tablet hardness by Partial least squares (PLS) and principal component regression (PCR) analysis. Results and Conclusion: Cross-validation with independent samples shows that PLS is the optimal predictive model. Which R2=0.9832, RSECV=0.334 and RSE=0.0669. This study established a new, simple, rapid, nondestructive and reliable methodology to optimize the IBU tablet hardness.

scholarly journals The Use of Multispectral Imaging and Single Seed and Bulk Near-Infrared Spectroscopy to Characterize Seed Covering Structures: Methods and Applications in Seed Testing and Research

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 301
Author(s):  
Anders Krogh Mortensen ◽  
René Gislum ◽  
Johannes Ravn Jørgensen ◽  
Birte Boelt
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Seed Quality ◽  
Cost Effective ◽  
Seed Testing ◽  
Seed Industry ◽  
Seed Health ◽  
Potential Benefits

The objective of seed testing is to provide high-quality seeds in terms of high varietal identity and purity, germination capacity, and seed health. Across the seed industry, it is widely acknowledged that quality assessment needs an upgrade and improvement by inclusion of faster and more cost-effective techniques. Consequently, there is a need to develop and apply new techniques alongside the classical testing methods, to increase efficiency, reduce analysis time, and meet the needs of stakeholders in seed testing. Multispectral imaging (MSI) and near-infrared spectroscopy (NIRS) are both quick and non-destructive methods that attract attention in seed research and in the seed industry. This review addresses the potential benefits and challenges of using MSI and NIRS for seed testing with a comprehensive focus on applications in physical and physiological seed quality as well as seed health.

scholarly journals Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jingzhe Wang ◽  
Tashpolat Tiyip ◽  
Jianli Ding ◽  
Dong Zhang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Organic Matter ◽  
Fractional Derivative ◽  
Near Infrared ◽  
Quantitative Estimation ◽  
Organic Matter Content ◽  
Nir Spectroscopy ◽  
Predictive Performance ◽  
Matter Content ◽  
Lake Basin ◽  
Least Squares Regression

Soil organic matter (SOM) content is an important index to measure the level of soil function and soil quality. However, conventional studies on estimation of SOM content concerned about the classic integer derivative of spectral data, while the fractional derivative information was ignored. In this research, a total of 103 soil samples were collected in the Ebinur Lake basin, Xinjiang Uighur Autonomous Region, China. After measuring the Vis-NIR (visible and near-infrared) spectroscopy and SOM content indoor, the raw reflectance and absorbance were treated by fractional derivative from 0 to 2nd order (order interval 0.2). Partial least squares regression (PLSR) was applied for model calibration, and five commonly used precision indices were used to assess the performance of these 22 models. The results showed that with the rise of order, these parameters showed the increasing or decreasing trends with vibration and reached the optimal values at the fractional order. A most robust model was calibrated based on 1.8 order derivative of R, with the lowest RMSEC (3.35 g kg−1) and RMSEP (2.70 g kg−1) and highest Rc2 (0.92), Rp2 (0.91), and RPD (3.42 > 3.0). This model had excellent predictive performance of estimating SOM content in the study area.

scholarly journals Total Anthocyanin Content in Intact Açaí (Euterpe oleracea Mart.) and Juçara (Euterpe edulis Mart.) Fruit Predicted by Near Infrared Spectroscopy

HortScience ◽  
2015 ◽  
Vol 50 (8) ◽  
pp. 1218-1223 ◽  
Author(s):  
Gustavo H. de A. Teixeira ◽  
Valquiria G. Lopes ◽  
Luís C. Cunha Júnior ◽  
José D.C. Pessoa
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Data Sets ◽  
Anthocyanin Content ◽  
Euterpe Oleracea ◽  
Total Anthocyanin ◽  
Euterpe Edulis ◽  
On Line ◽  
Aoac Method

Açaí (Euterpe oleraceae Mart.) and juçara (Euterpe edulis Mart.) palms are native to Brazil and these species are rich in anthocynanins. The methods applied to determine anthocyanins are time-consuming, generate chemical residues, and do not fit in modern on-line grading machines. As near infrared (NIR) spectroscopy has been used as a nondestructive method to determine anthocyanin, the objective of this study was to use NIR spectroscopy to predict total anthocyanin (TA) in intact açaí and juçara fruits. Spectra were collected using a Fourier transform (FT)-NIR spectrophotometer in the diffuse reflectance (4,000–10,000 cm−1) and TA reference data were obtained using the Association of Official Analytical Chemists (AOAC) method. Different treatments were applied to spectra and spectral data sets were correlated with TA by using partial least squares (PLSs) regression algorithm. The global-PLS model obtained with açaí and juçara spectra resulted in a root mean standard error of prediction (RMSEP) of 10.05 g·kg−1. However, this model was not adequate for TA levels found in açaí fruits, therefore individual models were developed. The açaí-PLS model proved to be more adequate, as RMSEP was reduced to 3.56 g·kg−1. On the other hand, the RMSEP obtained with the juçara-PLS model (6.59 g·kg−1) was almost the same of the global model. NIR spectroscopy can be used to adequately predict TA content in intact açaí and juçara fruits and this method could be used as an analytical procedure to monitor their quality.

scholarly journals MODEL PENDUGAAN KANDUNGAN AIR, LEMAK DAN ASAM LEMAK BEBAS PADA TIGA PROVENAN BIJI JARAK PAGAR (Jatropha curcas L.) MENGGUNAKAN SPEKTROSKOPI INFRAMERAH DEKAT DENGAN METODE PARTIAL LEAST SQUARE (PLS)

2020 ◽  
Vol 19 (4) ◽  
pp. 203
Author(s):  
LADY C. E. CH. LENGKEY ◽  
I WAYAN BUDIASTRA ◽  
KUDANG B. SEMINAR ◽  
BAMBANG S. PURWOKO
Keyword(s):  
Moisture Content ◽  
Standard Error ◽  
Near Infrared ◽  
Seed Quality ◽  
Nir Spectroscopy ◽  
Fat Content ◽  
Partial Least Square ◽  
Least Square ◽  
Physic Nut ◽  
Near Infrared Reflectance

<p>ABSTRAK<br />Jarak pagar berpotensi sebagai sumber biodiesel karena kandungan<br />lemak yang tinggi (&gt;40%) dan belum ada penggunaan lainnya.<br />Spektroskopi (Near Infrared) NIR adalah metode yang cepat untuk<br />mengukur spektrum sampel dan tidak terdapat limbah kimia. Tujuan<br />penelitian adalah mengembangkan metode pendugaan komposisi kimia<br />beberapa  provenan  jarak  pagar  berdasarkan  spektroskopi  NIR<br />menggunakan kalibrasi PLS. Pengujian dilakukan menggunakan tiga<br />provenan jarak pagar yaitu IP-3A, IP-3M, dan IP-3P masing-masing 85<br />sampel. Spektrum reflektansi diukur menggunakan alat NIRFlex Solids<br />Petri pada panjang gelombang 1000–2500 nm. Sekitar ⅔ jumlah sampel<br />digunakan untuk mengembangkan persamaan kalibrasi dan ⅓ jumlah<br />sampel untuk validasi. Pra perlakuan data spektrum dilakukan dengan<br />normalisasi antara 0-1, turunan pertama Savitzky-Golay 9 titik dan<br />gabungan keduanya. Hasil penelitian menunjukkan spektroskopi NIR<br />dapat menduga kadar air, lemak, dan asam lemak bebas . Koefisien<br />korelasi (r) antara komponen kimia metode acuan dengan dugaan NIR<br />&gt;0,83 menunjukkan ketepatan model cukup baik (r kadar air=0,96, r kadar<br />lemak=0,92, dan r ALB=0,89 ). Konsistensi model kalibrasi kadar<br />air=94,85%, lemak=82,56%, dan ALB=87,80%. Koefisien keragaman<br />dugaan (Prediction Coeficient Variability/PCV) ketiga model &lt;10%<br />menunjukkan model yang dibangun cukup handal. Ratio of standard error<br />prediction to deviation (RPD) menunjukkan metode spektroskopi NIR<br />dapat digunakan untuk menentukan kadar air (RPD=3,30) dan lemak<br />(RPD=2,06). Model-model yang dikembangkan secara umum layak<br />untuk menentukan kadar air dan lemak biji jarak pagar, tetapi belum<br />optimal untuk penentuan kadar ALB biji jarak pagar.<br />Kata kunci: NIR , jarak pagar, kadar air, kadar lemak, kadar asam lemak<br />bebas</p><p>ABSTRACT<br />Physic nut is a potential source of biodiesel. It is high in fat content,<br />above 40% and has not been usesed for other purposes. Moisture, free fatty<br />acid, and fat content are the chemical compounds and determinant factor<br />for physic nut seed quality. The objective of this study was to develop a<br />method to predict chemical composition of physic nut by NIR<br />spectroscopy and PLS calibration. The study was conducted using three<br />provenances of physic nut, i.e. IP-3A, IP-3M, and IP-3P, with 85 samples<br />each. The wavelengths of near infrared reflectance ranged from 1000 to<br />2500 nm, and measured by NIR Flex Solids Petri Apparatus.<br />Approximately ⅔ of total samples were used for developing calibration<br />equation, while ⅓ of total samples for performing validation. Pre-treatment<br />of spectrum data was done by applying normalization, first derivative of<br />Savitzky–Golay 9 points, and as well as their combination. The results<br />showed that NIR spectroscopy performed acceptable prediction for<br />moisture and fat content. Correlation coefficients (r) between the reference<br />method and NIR prediction were 0.96 for moisture content, 0.92 for fat<br />content, and 0.89 for FFA and the consistency of the model were 94.85%<br />for moisture content, 82.56% for fat, and 87.80% for FFA. Prediction of<br />coefficient of variability (PCV) of the three models ≤10 % shows that the<br />models are reliable. Ratio of standard error prediction to deviation (RPD)<br />for moisture content has the potential to be used for screening (RPD=3.30)<br />though the fat content model has rough screening (RPD=2.06).<br />Key words: NIR, physic nut, moisture, fat, free fatty acid contents.</p>

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