Detecting Bruise Damage and Level of Severity in Apples Using a Contactless NIR Spectrometer

2020 ◽  
Vol 36 (3) ◽  
pp. 257-270
Author(s):  
Jean Frederic Isingizwe Nturambirwe ◽  
Helene H Nieuwoudt ◽  
Willem Jacobus Perold ◽  
Umezuruike Linus Opara
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Apple Fruit ◽  
Performance Model ◽  
Classification Model ◽  
Good Representation ◽  
List Type ◽  
Classification Models

HighlightsIn the Emission Head (EH) configuration differences in apple bruise severity were well captured.A good representation of new samples variability, in calibration, ensured robust quantitative PLS-DA models.EH mode with PLS-DA is an attractive spectroscopic option for inline apple sorting based on bruise damage. Abstract. Bruise damage in apples is very common and undesirable because it hinders consumer satisfaction and greatly contributes to food loss. Fast detection of bruise damage in fruit using spectroscopic systems is still problematic, especially in terms of quantitative and objective assessments of mechanical damage and standardization of bruise measurement method, among other issues. Non-destructive techniques among which is near infrared (NIR) spectroscopy are under development as a potential solution carrier for such issues. A study of bruise damage was conducted on three apple cultivars using Fourier transform (FT) near infrared spectroscopy in two configurations (‘emission head’ of Bruker’s Matrix-F and ‘integrating sphere’ of Bruker’s multipurpose analyzer, MPA). The emission head (EH) allows for contactless large sample (100 mm) exposure that simulates on-line applications, while the MPA (sample size: 22 mm) is commonly used for in-laboratory analysis of inhomogeneous material such as fruit. Bruise damages were mechanically induced in apples, bruise sizes measured physically and destructively. Partial least squares discriminant analysis (PLS-DA) was used to determine the differences captured by the scanning spectrometers in apple fruit tissues. Discriminant analysis revealed that in both sample acquisition modes, distinction between bruised and non-bruised apple fruit tissue was achieved with high (from 78% to 93%) accuracy of classification (ACcl) based solely on spectral data. The classification accuracy improved when individual cultivars were considered and ranged from 94% to 96%. Classification models were tested for robustness and showed that both cultivar and bruise severity had influence on classification models’ performance. The results showed ability of the emission head configuration in detecting bruises and differentiating between severity of bruises in apple fruit, thus making it a good candidate for use in rapid detection and quantitative assessment of bruising in apple on sorting lines. Possibilities for improving the classification model performance and ensuring their robustness for the EH were suggested. Keywords: Apple bruise, Discriminant analysis, Model performance, Model threshold, NIR spectroscopy.

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

Molecules ◽  
2019 ◽  
Vol 24 (8) ◽  
pp. 1550 ◽  
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.

Classification of fish species from different ecosystems using the near infrared diffuse reflectance spectra of otoliths

2020 ◽  
Vol 28 (4) ◽  
pp. 224-235
Author(s):  
Irina M Benson ◽  
Beverly K Barnett ◽  
Thomas E Helser
Keyword(s):  
Discriminant Analysis ◽  
Spatial Variability ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Otolith Chemistry ◽  
K Nearest Neighbor ◽  
Fisheries Science

Applications of Fourier transform near infrared (FT-NIR) spectroscopy in fisheries science are currently limited. This current analysis of otolith spectral data demonstrate the potential applicability of FT-NIR spectroscopy to otolith chemistry and spatial variability in fisheries science. The objective of this study was to examine the use of NIR spectroscopy as a tool to differentiate among marine fishes in four large marine ecosystems. We examined otoliths from 13 different species, with three of these species coming from different regions. Principal component analysis described the main directions along which the specimens were separated. The separation of species and their ecosystems may suggest interactions between fish phylogeny, ontogeny, and environmental conditions that can be evaluated using NIR spectroscopy. In order to discriminate spectra across ecosystems and species, four supervised classification model techniques were utilized: soft independent modelling of class analogies, support vector machine discriminant analysis, partial least squares discriminant analysis, and k-nearest neighbor analysis (KNN). This study showed that the best performing model to classify combined ecosystems, all four ecosystems, and species was the KNN model, which had an overall accuracy rate of 99.9%, 97.6%, and 91.5%, respectively. Results from this study suggest that further investigations are needed to determine applications of NIR spectroscopy to otolith chemistry and spatial variability.

Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy

2006 ◽  
Vol 57 (4) ◽  
pp. 403 ◽  
Author(s):  
Robert L. Long ◽  
Kerry B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Quality Standard ◽  
Sampling Procedure ◽  
Performance Model ◽  
Soluble Solids ◽  
Fruit Storage ◽  
Total Soluble Solids ◽  
Fruit Maturity

The imposition of a minimum total soluble solids (TSS) value as a quality standard for orange-flesh netted melon fruit (Cucumis melo L. reticulatus group) requires either a batch sampling procedure (i.e. the estimation of the mean and standard deviation of a population), or the individual assessment of fruit [e.g. using a non-destructive procedure such as near infrared (NIR) spectroscopy]. Several potential limitations to the NIR assessment of fruit, including the variation in TSS within fruit and the effect of fruit storage conditions on the robustness of calibration models, were considered in this study. Outer mesocarp TSS was 3 TSS units higher at the stylar end of the fruit compared with the stem end, and the TSS of inner mesocarp was higher than outer tissue and more uniform across spatial positions. The linear relationship between the outer 10 mm and the subsequent middle 10 mm of tissue varied with fruit maturity [e.g. 42 days before harvest (DBH), r 2 = 0.8; 13 DBH, r 2 = 0.4; 0 DBH, r 2 = 0.7], and with cultivars (at fruit maturity, Eastern Star 2001 r 2 = 0.88; Malibu 2001 r 2 = 0.59). This relationship notably affected NIR calibration performance (e.g. based on inner mesocarp TSS; R c 2 = 0.80, root mean standard error of cross-validation (RMSECV) = 0.65, and R c 2 = 0.41, RMSECV = 0.88 for mature Eastern Star and Malibu fruit, respectively). Cold storage of fruit (0–14 days at 5°C) did not affect NIR model performance. Model performance was equivalent when based on either that part of the fruit in contact with the ground or equatorial positions; however, it was improved when based on the stylar end of the fruit.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
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.

The Use of Visible and near Infrared Spectroscopy to Classify the Floral Origin of Honey Samples Produced in Uruguay

2005 ◽  
Vol 13 (2) ◽  
pp. 63-68 ◽  
Author(s):  
E. Corbella ◽  
D. Cozzolino
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Sample Preparation ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Models ◽  
Linear Discriminant

This study reports the use of visible (vis) and near infrared (NIR) spectroscopy as a tool to classify honey samples from Uruguay, according to their floral origin. Classification models were developed using principal component analysis, discriminant partial least squares (DPLS) regression and linear discriminant analysis (LDA). Honey samples ( n = 50) from two floral origins, namely Eucalyptus spp. and pasture, were split randomly into even calibration ( n = 25) and validation sets ( n = 25). Both LDA and DPLS models correctly classified, on average, more than 75% of the honey samples belonging to pasture and more than 85% of the honey samples belonging to Eucalyptus spp. These results showed that vis-NIR might be a suitable and alternative method that can easily be implemented by both the industry and retailers to classify samples according their floral origin. Vis-NIR analysis requires little sample preparation and is rapid. However, the relatively limited number of samples involved in the present work led us to be cautious in terms of extrapolating the results of this work to other floral types.

scholarly journals Assessment of chicken breast shelf life based on bench-top and portable NIR spectroscopy tools coupled with chemometrics

2020 ◽  
Author(s):  
Ilaria Lanza ◽  
Daniele Conficoni ◽  
Stefania Balzan ◽  
Marco Cullere ◽  
Luca Fasolato ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Shelf Life ◽  
Meat Quality ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Life Assessment ◽  
Chicken Breast ◽  
Portable Devices ◽  
Physicochemical Processes ◽  
Insight Into

Abstract Near-infrared (NIR) spectroscopy is a rapid technique able to assess meat quality even if its capability to determine the shelf life of chicken fresh cuts is still debated, especially for portable devices. The aim of the study was to compare bench-top and portable NIR instruments in discriminating between four chicken breast refrigeration times (RT), coupled with multivariate classifier models. Ninety-six samples were analysed by both NIR tools at 2, 6, 10 and 14 days post-mortem. NIR data were subsequently submitted to partial least squares discriminant analysis (PLS-DA) and canonical discriminant analysis (CDA). The latter was preceded by double feature selection based on Boruta and Stepwise procedures. PLS-DA sorted moderate separation of RT theses, while shelf life assessment was more accurate on application of Stepwise-CDA. Bench-top tool had better performance than portable one, probably because it captured more informative spectral data as shown by the variable importance in projection (VIP) and restricted pool of Stepwise-CDA predictive scores (SPS). NIR tools coupled with a multivariate model provide deep insight into the physicochemical processes occurring during storage. Spectroscopy showed reliable effectiveness to recognise a 7-day shelf life threshold of breasts, suitable for routine at-line application for screening of meat quality.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

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

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.

scholarly journals Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

2019 ◽  
Author(s):  
Aoife Gowen ◽  
Jun-Li Xu ◽  
Ana Herrero-Langreo
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Imaging Spectroscopy ◽  
Model Performance ◽  
Data Sampling ◽  
Classification Models ◽  
Imaging Data ◽  
Selection Of

Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection.

scholarly journals Discrimination of Salvia miltiorrhiza Bunge from Different Geographical Locations Employing High-Performance Liquid Chromatography, Near-Infrared Fingerprinting Combined with Chemometrics

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jiao Wang ◽  
Yichun Sun ◽  
Zhan Li ◽  
Wei Li ◽  
Yuanyuan Pang ◽  
...  
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Discriminant Analysis ◽  
High Performance ◽  
Near Infrared ◽  
Salvia Miltiorrhiza ◽  
Nir Spectroscopy ◽  
Gradient Elution ◽  
Array Detector ◽  
Geographical Locations

To evaluate the quality of Salvia miltiorrhiza Bunge, high-performance liquid chromatography-diode array detector (HPLC/UV-PAD), near infrared (NIR) spectroscopy, and chemometrics were used to discriminate nine components of samples from four different geographical locations. HPLC was performed with a C18 (5 μm, 4.6 mm × 250 mm) column and 0.1% formic acid aqueous solution-acetonitrile with a gradient elution system. Orthogonal partial least squares discriminant analysis was used to identify the amounts of salvianolic acid B. NIR was used to distinguish rapidly S. miltiorrhiza Bunge samples from different geographical locations. In this assay, discriminant analysis was performed, and the accuracy was found to be 100%. The combination of these two methods can be used to quickly and accurately identify S. miltiorrhiza Bunge from different geographical locations.

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