Non-Destructive Quality Measurement for Three Varieties of Tomato Using VIS/NIR Spectroscopy

The non-destructive visible/near-infrared (VIS/NIR) spectroscopy is a promising technique in determining the quality of agricultural commodities. Therefore, this study aimed to examine the ability of VIS/NIR spectroscopy (550–1100 nm) to distinguish between three different varieties of tomato (i.e., Ekram, Harver and Izmer), as well as to predict the quality parameters of tomato, such as soluble solids content (SSC), titratable acidity (TA), taste (SSC/TA) and firmness. Ninety intact samples from three tomato varieties were used. These samples were examined using VIS/NIR spectroscopy and quality parameters were also measured using traditional methods. Principal component analysis (PCA) and partial least square (PLS) were carried out. The results of PCA showed the ability of VIS/NIR spectroscopy to distinguish between the three varieties, where two PCs explained about 99% of the total variance in both calibration and validation sets. Moreover, PLS showed the possibility of modelling quality parameters. The correlation coefficient (R2) and the ratio of performance deviation (RPD) for all quality parameters (except for firmness) were found to be higher than 0.85 and 2.5, respectively. Thus, these results indicate that the VIS/NIR spectroscopy can be used to discriminate between different varieties of tomato and predict their quality parameters.

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Application of the Non-Destructive NIR Technique for the Evaluation of Strawberry Fruits Quality Parameters

Foods ◽

10.3390/foods9040441 ◽

2020 ◽

Vol 9 (4) ◽

pp. 441 ◽

Cited By ~ 2

Author(s):

Manuela Mancini ◽

Luca Mazzoni ◽

Francesco Gagliardi ◽

Francesca Balducci ◽

Daniele Duca ◽

...

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Infrared Region ◽

Quality Parameters ◽

Soluble Solids ◽

Electromagnetic Spectrum ◽

Least Squares Regression ◽

Solids Content ◽

Non Destructive

The determination of strawberry fruit quality through the traditional destructive lab techniques has some limitations related to the amplitude of the samples, the timing and the applicability along all phases of the supply chain. The aim of this study was to determine the main qualitative characteristics through traditional lab destructive techniques and Near Infrared Spectroscopy (NIR) in fruits of five strawberry genotypes. Principal Component Analysis (PCA) was applied to search for spectral differences among all the collected samples. A Partial Least Squares regression (PLS) technique was computed in order to predict the quality parameters of interest. The PLS model for the soluble solids content prediction was the best performing—in fact, it is a robust and reliable model and the validation values suggested possibilities for its use in quality applications. A suitable PLS model is also obtained for the firmness prediction—the validation values tend to worsen slightly but can still be accepted in screening applications. NIR spectroscopy represents an important alternative to destructive techniques, using the infrared region of the electromagnetic spectrum to investigate in a non-destructive way the chemical–physical properties of the samples, finding remarkable applications in the agro-food market.

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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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Nondestructive Detection of Soluble Solids Content of Nanfeng Mandarin Orange Using VIS-NIR Spectroscopy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.361-363.1634 ◽

2011 ◽

Vol 361-363 ◽

pp. 1634-1637 ◽

Cited By ~ 1

Author(s):

Lu Zhang ◽

Long Xue ◽

Mu Hua Liu ◽

Jing Li

Keyword(s):

Near Infrared ◽

Scatter Correction ◽

Nir Spectroscopy ◽

Rapid Analysis ◽

Partial Least Square ◽

Least Square ◽

Soluble Solids ◽

Soluble Solids Content ◽

Solids Content ◽

Mandarin Orange

This study demonstrated how VIS-NIR spectroscopy can be used in the quantitative, noninvasive probing of soluble solids content (SSC) of mandarin orange. Total 197 mandarin oranges were divided into calibration set (133 samples) and prediction set (64 samples). Multiple scatter correction (MSC) was used to preprocess the collected visible and near infrared (Vis-NIR) spectra (350-1800nm) of mandarin orange. Partial least square (PLS), interval partial least square (IPLS) and synergy interval partial least square (SIPLS) methods were applied for constructing predictive models of SSC. Experimental results showed that the optimal SIPLS model obtained with 10 PLS components and the optimal combinations of intervals were number 5,7,8,9. The correlation coefficient (r) between the predicted and actual SSC was 0.9265 and 0.8577 for calibration and prediction set, respectively. The root mean square error of calibration (RMSEC) and prediction (RMSEP) set was 0.4890 and 0.7113, respectively. In conclusion, the combination of Vis-NIR spectroscopy and SIPLS methods can be used to provide a technique of noninvasive, convenient and rapid analysis for SSC in fruit.

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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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Application of near Infrared Spectroscopy for the Quantification of Quality Parameters in Selected Vegetables and Essential Oil Plants

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.179 ◽

1998 ◽

Vol 6 (A) ◽

pp. A125-A130 ◽

Cited By ~ 29

Author(s):

H. Schulz ◽

H.-H. Drews ◽

R. Quilitzsch ◽

H. Krüger

Keyword(s):

Essential Oil ◽

Oil Content ◽

Near Infrared ◽

Nir Spectroscopy ◽

Quality Parameters ◽

Partial Least Square ◽

Least Square ◽

Total Carotenoids ◽

Good Calibration

The use of near-infrared (NIR) spectroscopy for the prediction of the essential oil content and composition in various umbelliferae genotypes was investigated. Furthermore an NIR method was developed for the quantification of total carotenoids and sugars present in different carrot varieties. Applying partial least square algorithm very good calibration statistics ( SECV, R2) were obtained for the prediction of the essential oil content in fennel (0.47, 0.83), caraway (0.29, 0.93), dill (0.30, 0.96) and coriander (0.29, 0.93). Satisfactory calibration results were received for the NIR determination of total carotenoids (1.54, 0.80) and of saccharose(0.74, 0.76) in carrots. The performed study demonstrates that NIR can be used to rapidly and accurately predict secondary metabolites such as carotenoids, anethole, fenchone, estragole, limonene and carvone occurring in vegetables and in fruits of various essential oil plants.

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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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Rapid Determination of β-Glucan Content of Hulled and Naked Oats Using near Infrared Spectroscopy Combined with Chemometrics

Foods ◽

10.3390/foods11010043 ◽

2021 ◽

Vol 11 (1) ◽

pp. 43

Author(s):

Maninder Meenu ◽

Yaqian Zhang ◽

Uma Kamboj ◽

Shifeng Zhao ◽

Lixia Cao ◽

...

Keyword(s):

Near Infrared ◽

Rapid Determination ◽

Wavelength Region ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Naked Oats ◽

Non Destructive ◽

Selection Of

The quantification of β-glucan in oats is of immense importance for plant breeders and food scientists to develop plant varieties and food products with a high quantity of β-glucan. However, the chemical analysis of β-glucan is time consuming, destructive, and laborious. In this study, near-infrared (NIR) spectroscopy in conjunction with Chemometrics was employed for rapid and non-destructive prediction of β-glucan content in oats. The interval Partial Least Square (iPLS) along with correlation matrix plots were employed to analyze the NIR spectrum from 700–1300 nm, 1300–1900 nm, and 1900–2500 nm for the selection of important wavelengths for the prediction of β-glucan. The NIR spectral data were pre-treated using Savitzky Golay smoothening and normalization before employing partial least square regression (PLSR) analysis. The PLSR models were established based on the selection of wavelengths from PLS loading plots that present a high correlation with β-glucan content. It was observed that wavelength region 700–1300 nm is sufficient for the satisfactory prediction of β-glucan of hulled and naked oats with R2c of 0.789 and 0.677, respectively, and RMSE < 0.229.

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Evaluation of Quality Parameters of Apple Juices Using Near-Infrared Spectroscopy and Chemometrics

Journal of Spectroscopy ◽

10.1155/2018/5191283 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Katarzyna Włodarska ◽

Igor Khmelinskii ◽

Ewa Sikorska

Keyword(s):

Near Infrared ◽

Cross Validation ◽

Nir Spectroscopy ◽

Titratable Acidity ◽

Quality Parameters ◽

Soluble Solids ◽

Soluble Solids Content ◽

Model Optimization ◽

Important Quality ◽

Solids Content

Near-infrared (NIR) spectra were recorded for commercial apple juices. Analysis of these spectra using partial least squares (PLS) regression revealed quantitative relations between the spectra and quality- and taste-related properties of juices: soluble solids content (SSC), titratable acidity (TA), and the ratio of soluble solids content to titratable acidity (SSC/TA). Various spectral preprocessing methods were used for model optimization. The optimal spectral variables were chosen using the jack-knife-based method and different variants of the interval PLS (iPLS) method. The models were cross-validated and evaluated based on the determination coefficients (R2), root-mean-square error of cross-validation (RMSECV), and relative error (RE). The best model for the prediction of SSC (R2 = 0.881, RMSECV = 0.277 °Brix, and RE = 2.37%) was obtained for the first-derivative preprocessed spectra and jack-knife variable selection. The optimal model for TA (R2 = 0.761, RMSECV = 0.239 g/L, and RE = 4.55%) was obtained for smoothed spectra in the range of 6224–5350 cm−1. The best model for the SSC/TA (R2 = 0.843, RMSECV = 0.113, and RE = 5.04%) was obtained for the spectra without preprocessing in the range of 6224–5350 cm−1. The present results show the potential of the NIR spectroscopy for screening the important quality parameters of apple juices.

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