Classification Method for Viability Screening of Naturally Aged Watermelon Seeds Using FT-NIR Spectroscopy

Viability analysis of stored seeds before sowing has a great importance as plant seeds lose their viability when they exposed to long term storage. In this study, the potential of Fourier transform near infrared spectroscopy (FT-NIR) was investigated to discriminate between viable and non-viable triploid watermelon seeds of three different varieties stored for four years (natural aging) in controlled conditions. Because of the thick seed-coat of triploid watermelon seeds, penetration depth of FT-NIR light source was first confirmed to ensure seed embryo spectra can be collected effectively. The collected spectral data were divided into viable and nonviable groups after the viability being confirmed by conducting a standard germination test. The obtained results showed that the developed partial least discriminant analysis (PLS-DA) model had high classification accuracy where the dataset was made after mixing three different varieties of watermelon seeds. Finally, developed model was evaluated with an external data set (collected at different time) of hundred samples selected randomly from three varieties. The results yield a good classification accuracy for both viable (87.7%) and nonviable seeds (82%), thus the developed model can be considered as a “general model” since it can be applied to three different varieties of seeds and data collected at different time.

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Physicochemical Fingerprint of “Pera Rocha do Oeste”. A PDO Pear Native from Portugal

Foods ◽

10.3390/foods9091209 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1209

Author(s):

Soraia I. Pedro ◽

Elisabete Coelho ◽

Fátima Peres ◽

Ana Machado ◽

António M. Rodrigues ◽

...

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

World Market ◽

Soluble Solids ◽

Total Phenols ◽

Atmospheric Conditions ◽

Data Set ◽

External Data ◽

Protected Designation Of Origin ◽

Ripening Index

“Pera Rocha do Oeste” is a pear (Pyrus communis L.) variety native from Portugal with a Protected Designation of Origin (PDO). To supply the world market for almost all the year, the fruits are kept under controlled storage. This study aims to identify which classical physicochemical parameters (colour, total soluble solids (TSS), pH, acidity, ripening index, firmness, vitamin C, total phenols, protein, lipids, fibre, ash, other compounds including carbohydrates, and energy) could be fingerprint markers of PDO “Pera Rocha do Oeste”. For this purpose, a data set constituting fruits from the same size, harvested from three orchards of the most representative PDO locations and stored in refrigerated conditions for 2 or 5 months at atmospheric conditions or for 5 months under a modified atmosphere, were selected. To validate the fingerprint parameters selected with the first set, an external data set was used with pears from five PDO orchards stored under different refrigerated conditions. Near infrared (NIR) spectroscopy was used as a complementary tool to assess the global variability of the samples. The lightness of the pulp; the b* CIELab coordinate of the pulp and peel; and the pulp TSS, pH, firmness, and total phenols, due to their lower variability, are proposed as fingerprint markers of this pear.

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Biodiesel Synthesis Monitoring using Near Infrared Spectroscopy

The Open Chemical Engineering Journal ◽

10.2174/1874123101812010095 ◽

2018 ◽

Vol 12 (1) ◽

pp. 95-110 ◽

Cited By ~ 1

Author(s):

Estela Kamile Gelinski ◽

Fabiane Hamerski ◽

Marcos Lúcio Corazza ◽

Alexandre Ferreira Santos

Keyword(s):

Fossil Fuels ◽

Near Infrared ◽

Reference Method ◽

Nir Spectroscopy ◽

Transesterification Reaction ◽

Biodiesel Production ◽

Molar Ratios ◽

External Data ◽

Biodiesel Synthesis ◽

Biodiesel Production Monitoring

Objective: Biodiesel is a renewable fuel considered as the main substitute for fossil fuels. Its industrial production is mainly made by the transesterification reaction. In most processes, information on the production of biodiesel is essentially done by off-line measurements. Methods: However, for the purpose of control, where online monitoring of biodiesel conversion is required, this is not a satisfactory approach. An alternative technique to the online quantification of conversion is the near infrared (NIR) spectroscopy, which is fast and accurate. In this work, models for biodiesel reactions monitoring using NIR spectroscopy were developed based on the ester content during alkali-catalyzed transesterification reaction between soybean oil and ethanol. Gas chromatography with flame ionization detection was employed as the reference method for quantification. FT-NIR spectra were acquired with a transflectance probe. The models were developed using Partial Least Squares (PLS) regression with synthetic samples at room temperature simulating reaction composition for different ethanol to oil molar ratios and conversions. Model predictions were then validated online for reactions performed with ethanol to oil molar ratios of 6 and 9 at 55ºC. Standard errors of prediction of external data were equal to 3.12%, hence close to the experimental error of the reference technique (2.78%), showing that even without using data from a monitored reaction to perform calibration, proper on-line predictions were provided during transesterification runs. Results: Additionally, it is shown that PLS models and NIR spectra of few samples can be combined to accurately predict the glycerol contents of the medium, making the NIR spectroscopy a powerful tool for biodiesel production monitoring.

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Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

10.5194/egusphere-egu21-15021 ◽

2021 ◽

Author(s):

Hayfa Zayani ◽

Youssef Fouad ◽

Didier Michot ◽

Zeineb Kassouk ◽

Zohra Lili-Chabaane ◽

...

Keyword(s):

Organic Carbon ◽

Near Infrared ◽

Full Range ◽

Nir Spectroscopy ◽

Soil Samples ◽

Calibration Data ◽

Least Squares Regression ◽

Data Set ◽

Soil Carbon Stocks ◽

The Mean

Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40&#176;C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec&#174;3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation &#8710;SOCobsobtained from observed values in 2013 and 2018 (&#8710;SOCobs = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R&#178;= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed &#8710;SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which &#8710;SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.

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Application of near-infrared spectroscopy for the detection of internal insect infestation inPiceaabiesseed lots

Canadian Journal of Forest Research ◽

10.1139/x03-189 ◽

2004 ◽

Vol 34 (1) ◽

pp. 76-84 ◽

Cited By ~ 17

Author(s):

Mulualem Tigabu ◽

Per Christer Odén ◽

Tong Yun Shen

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Least Squares Regression ◽

Absorption Bands ◽

Classification Rate ◽

Data Set ◽

Storage Lipids ◽

Nir Absorption ◽

Seed Origin ◽

Single Seeds

The use of near-infrared (NIR) spectroscopy to discriminate between uninfested seeds of Picea abies (L.) Karst and seeds infested with Plemeliella abietina Seitn (Hymenoptera, Torymidae) larva is sensitive to seed origin and year of collection. Five seed lots collected during different years from Sweden, Finland, and Belarus were used in this study. Initially, seeds were classified as infested or uninfested with X-radiography, and then, NIR spectra from single seeds were collected with a NIR spectrometer from 1100 to 2498 nm with a resolution of 2 nm. Discriminant models were derived by partial least squares regression using raw and orthogonal signal corrected spectra (OSC). The resulting OSC model developed on a pooled data set was more robust than the raw model and resulted in 100% classification accuracy. Once irrelevant spectral variations were removed by using OSC pretreatment, single-lot calibration models resulted in similar classification rates for the new samples irrespective of origin and year of collection. Dis criminant analyses performed with selected NIR absorption bands also gave nearly 100% classification rate for new samples. The origin of spectral differences between infested and uninfested seeds was attributed to storage lipids and proteins that were completely depleted in the former by the feeding larva.

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Discrimination of Trichosanthis Fructus from Different Geographical Origins Using Near Infrared Spectroscopy Coupled with Chemometric Techniques

Molecules ◽

10.3390/molecules24081550 ◽

2019 ◽

Vol 24 (8) ◽

pp. 1550 ◽

Cited By ~ 3

Author(s):

Liang Xu ◽

Wen Sun ◽

Cui Wu ◽

Yucui Ma ◽

Zhimao Chao

Keyword(s):

Discriminant Analysis ◽

Classification Accuracy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Classification Model ◽

Support Vector ◽

Model Parameters ◽

Classification Models ◽

Crude Drugs ◽

Chemometric Techniques

Near infrared (NIR) spectroscopy with chemometric techniques was applied to discriminate the geographical origins of crude drugs (i.e., dried ripe fruits of Trichosanthes kirilowii) and prepared slices of Trichosanthis Fructus in this work. The crude drug samples (120 batches) from four growing regions (i.e., Shandong, Shanxi, Hebei, and Henan Provinces) were collected, dried, and used and the prepared slice samples (30 batches) were purchased from different drug stores. The raw NIR spectra were acquired and preprocessed with multiplicative scatter correction (MSC). Principal component analysis (PCA) was used to extract relevant information from the spectral data and gave visible cluster trends. Four different classification models, namely K-nearest neighbor (KNN), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and support vector machine-discriminant analysis (SVM-DA), were constructed and their performances were compared. The corresponding classification model parameters were optimized by cross-validation (CV). Among the four classification models, SVM-DA model was superior over the other models with a classification accuracy up to 100% for both the calibration set and the prediction set. The optimal SVM-DA model was achieved when C =100, γ = 0.00316, and the number of principal components (PCs) = 6. While PLS-DA model had the classification accuracy of 95% for the calibration set and 98% for the prediction set. The KNN model had a classification accuracy of 92% for the calibration set and 94% for prediction set. The non-linear classification method was superior to the linear ones. Generally, the results demonstrated that the crude drugs from different geographical origins and the crude drugs and prepared slices of Trichosanthis Fructus could be distinguished by NIR spectroscopy coupled with SVM-DA model rapidly, nondestructively, and reliably.

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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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Estimating the Composition of Tomato Juice Products by near Infrared Spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.360 ◽

2003 ◽

Vol 11 (2) ◽

pp. 123-136 ◽

Cited By ~ 12

Author(s):

Athanasia M. Goula ◽

Konstantinos G. Adamopoulos

Keyword(s):

Near Infrared ◽

Correlation Coefficients ◽

Nir Spectroscopy ◽

Tomato Juice ◽

Calibration Data ◽

Multilinear Regression ◽

Statistical Parameters ◽

Data Set ◽

Prediction Ability ◽

Specific Product

The use of near infrared (NIR) reflectance spectroscopy for the rapid and accurate measurement of moisture, sugar, acid, protein and salt was explored in a diverse group of tomato juice products. Partial and overall calibrations were performed on four different tomato juice products. Partial calibrations for each product included samples of the specific product, whereas overall calibration used samples of all the products. Samples were analysed employing traditional chemical methods and scanned using an Instalab 600-Dickey-John NIR apparatus to obtain NIR spectra. Calibrations were achieved with the use of multilinear regression between chemical and spectral data from each calibration data set. A separate set of samples was used to validate the calibrations. Linear regression was applied to compare the results obtained by NIR spectroscopy for all constituents of the validation set with those obtained by the reference methods. In addition, the root mean square error of prediction ( RMSEP), the bias and the correlation coefficients ( r and r′) were calculated. All of the statistical parameters were better with overall than with partial calibrations. Prediction ability of overall calibration was very good for all the constituents. r and r′ values were higher than 0.9488 and 0.9453, respectively, RMSEP values were smaller than 0.1067, whereas bias varied from −0.020 to 0.016. The partial calibrations are considerable less variable with the correlation coefficients r and r′ ranged from 0.8890 to 0.9477 and from 0.7202 to 0.8518, respectively, RMSEP varied from 0.0647 to 0.4942 and bias from −0.365 to 0.071. NIR measurement as performed by the Dickey-John Analyser was proved a rapid and accurate method for analysis of tomato juice samples and may be used as a replacement for conventional expensive and time-consuming wet chemistry methods.

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Discrimination of Storage Duration of Apples Stored in a Cooled Room and Shelf-Life by Visible-Near Infrared Spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.726 ◽

2007 ◽

Vol 15 (3) ◽

pp. 169-177 ◽

Cited By ~ 24

Author(s):

C. Camps ◽

P. Guillermin ◽

J.C. Mauget ◽

D. Bertrand

Keyword(s):

Spectral Data ◽

Near Infrared ◽

Nir Spectroscopy ◽

Absorption Bands ◽

Storage Duration ◽

Data Set ◽

Global Models ◽

Duration Of Storage ◽

Apple Cultivars

Improved non-destructive instrumental approaches for grading fruit during post-harvest could be an efficient way to monitor stock in the apple industry. The objective of this study was to evaluate the ability of visible-near infrared (vis-NIR) spectroscopy in reflectance mode for classifying apples left on the shelf or stored in a cooled room. The ability of NIR spectroscopy to classify the duration of storage of three apple cultivars in two storage modalities was evaluated. A total of 450 fruit, sampled after 7, 14, 28, 60, 90 and 120 days of storage in a cooled room (CR) and 7, 14 and 28 days in shelflife (SL), has been studied. The classification of these modalities was analysed by factorial discriminant analysis (FDA) pooling the spectral data of all cultivars (global models) into a common data set. Then, the cultivar effect on the classification of the same modalities was analysed by processing data from each cultivar in separate factorial descriminant analyses. A preliminary analysis showed the genetic variability of spectral data due to the three apple cultivars. We show that vis-NIR spectroscopy allowed the correct classification of the fruits of each cultivar by more than 95%. The classification relied on both vis and NIR absorption bands: 500, 680, 1400 to 1700, 1850, 1950, 2200 and 2300 nm. We show that storage modalities of global models can be classified by more than 75% and 83% for fruits stored in a cooled room and shelf, respectively. Classification of the same storage modalities was improved by cultivar models with percentage of individuals correctly classified of 86% (Gala), 89% (Elstar) and 85% (Smoothee) for fruits stored in a cooled room and 95% (Gala), 98% (Elstar) and 95% (Smoothee) for fruits left in shelflife. We conclude that despite the slight increase of efficiency of the models when we considered each apple cultivar separately, global models applicable to a set of different cultivars presents a correct level of classification and could be usefull for some commercial applications.

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A Comparison of near Infrared Method Development Approaches Using a Drug Product on Different Spectrophotometers and Chemometric Software Algorithms

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.854 ◽

2009 ◽

Vol 17 (5) ◽

pp. 233-244 ◽

Cited By ~ 5

Author(s):

Assad Kazeminy ◽

Saeed Hashemi ◽

Roger L. Williams ◽

Gary E. Ritchie ◽

Ronald Rubinovitz ◽

...

Keyword(s):

Method Development ◽

Near Infrared ◽

Nir Spectroscopy ◽

Dosage Forms ◽

Multivariate Models ◽

Data Set ◽

Pharmaceutical Dosage Forms ◽

Pre Treatment ◽

Instrument Configuration

It is well known that spectral variability in near infrared (NIR) spectroscopy can be attributed to the analyst, sample, sample positioning, instrument configuration and software (in both algorithm formats and structures used as well as in the execution of data pre-treatment and analysis). It is often acknowledged that the single largest factor impacting NIR results is sample presentation. However, what is obvious but not often acknowledged is that there are instrumental and software differences as well. These differences, evident in the quality of the spectra, may impact the chemometrics that are subsequently performed and, possibly, the results obtained from the multivariate statistical models. In order to investigate just what are these sources of variability, and just how much these variations may impact the results of the multivariate models for predicting the identification of pharmaceutical dosage forms, a study has been conducted. To the authors' knowledge, no other systematic study of this kind has been published. In this study, we are interested in learning what variability, if any, the choices for instrument and software have on the development of a NIR method for the identification of pharmaceutical dosage forms. Furthermore, we would like to learn what and how do the choices made early on in the experimental design impact the final quality of the spectra and the resulting multivariate models obtained from these spectra. A study protocol was designed, using a common data set consisting of four formulations of Ibuprofen, involving three investigating parties, namely, US FDA, USP and Irvine Pharmaceutical Services and using three NIR instruments, namely (listed in alphabetical order), a Bruker spectrometer, a Büchi spectrometer and a Foss spectrometer. Based on the results and despite differences in instrument configuration [dispersive or Fourier Transform (FT)], number of spectral data points, principal components analysis (PCA) or factorisation algorithms, and validation modelling approach, exact and accurate spectroscopic results can be achieved using NIR spectroscopy for discriminate analysis. More importantly, this study shows that the same NIR method spectral range and pre-treatment parameters can be used, and that nearly the same multivariate models can be obtained, despite instrumental and software differences, to accurately predict the identity of pharmaceutical dosage forms.

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Rapid Detection of Melamine in Milk Powder by near Infrared Spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.829 ◽

2009 ◽

Vol 17 (2) ◽

pp. 59-67 ◽

Cited By ~ 57

Author(s):

Chenghui Lu ◽

Bingren Xiang ◽

Gang Hao ◽

Jianping Xu ◽

Zhengwu Wang ◽

...

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Rapid Detection ◽

Near Infrared ◽

Milk Powder ◽

Nir Spectroscopy ◽

Accurate Method ◽

Partial Least Square ◽

Least Square ◽

Support Vector

This paper establishes a novel and rapid method for detecting pure melamine in milk powder using near infrared (NIR) spectroscopy based on least squares-support vector machine (LS-SVM). Partial least square discriminant analysis (PLS-DA) was used for the extraction of principal components (PCs). The scores of the first two PCs have been applied as inputs to LS-SVM. Compared to PLS-DA, the performance of LS-SVM was better, with higher classification accuracy, both 100% for the training and testing set. The detection limit was lower than 1 ppm. Based on the results, it was concluded that NIR spectroscopy combined with LS-SVM could be used as a rapid and accurate method for detecting pure melamine in milk powder.

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