Potential of fourier transformed near-infrared (FT-NIR) spectroscopy for rapid analysis of elderberry (Sambucus nigra L.) fruits

Fruit samples were analysed to investigate the suitability of Fourier transform near infrared spectroscopy (FT-NIR) for the rapid discrimination of elderberry genotypes. Parallel analysis with classical chemical techniques and spectral measurements was performed on 11 cultivars originating from various European countries. The titratable acidity (TA) and soluble solids content (SSC) of the fruit, and the geographical origin and breeding method of the cultivar were used as reference data. Three spectrum transformation methods (standard normal variation, multiplicative scatter correction and first derivative) were applied in the calibration process. The statistical analysis and comparison of the samples was carried out using principal component analysis (PCA) and linear discriminant analysis (LDA). In all cases the analysis demonstrated a correlation between the spectra and both the chemical traits (TA and SSC) of the fruit and the other reference data, indicating that pattern recognition was not a chance occurrence. This work provides the first evidence that the NIR technique can be successfully applied to distinguish between elderberry genotypes on the basis of fruit quality, thus opening up new possibilities in breeding cultivars for food industry purposes.

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

Sustainability ◽

10.3390/su131910747 ◽

2021 ◽

Vol 13 (19) ◽

pp. 10747

Author(s):

Khadija Najjar ◽

Nawaf Abu-Khalaf

Keyword(s):

Near Infrared ◽

Quality Measurement ◽

Nir Spectroscopy ◽

Principal Component ◽

Titratable Acidity ◽

Quality Parameters ◽

Partial Least Square ◽

Least Square ◽

Soluble Solids ◽

Non Destructive

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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Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin

Journal of Spectroscopy ◽

10.1155/2018/7652592 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Hao Zhang ◽

Haifeng Sun ◽

Ling Wang ◽

Shun Wang ◽

Wei Zhang ◽

...

Keyword(s):

Pattern Recognition ◽

Near Infrared ◽

Scatter Correction ◽

Recognition Rate ◽

Nir Spectroscopy ◽

Principal Component ◽

Support Vector ◽

Linear Discriminant ◽

Pattern Recognition Methods ◽

Gelatin Gels

The aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. Principal component analysis (PCA) was first carried out for spectral analysis, while the six gelatin categories could not be clearly distinguished. Further, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), backpropagation neural network (BPNN), and support vector machine (SVM) were introduced to establish discrimination models for identifying the adulterated gelatin gels, which gave a total correct recognition rate of 97.44%, 100%, 97.44%, and 100%, respectively. For the SIMCA model with significant level α = 0.05, sample overlapping clustering appeared; thus, the SVM model presents the best recognition ability among these four discrimination models for the classification of edible gelatin adulteration. The results demonstrate that NIR spectroscopy combined with unsupervised pattern recognition methods can quickly and accurately identify edible gelatin with different adulteration levels, providing a new possibility for the detection of industrial gelatin illegally added into food products.

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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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The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

Applied Spectroscopy ◽

10.1366/0003702884429869 ◽

1988 ◽

Vol 42 (7) ◽

pp. 1273-1284 ◽

Cited By ~ 362

Author(s):

Tomas Isaksson ◽

Tormod Næs

Keyword(s):

Near Infrared ◽

Scatter Correction ◽

Principal Component Regression ◽

Nir Spectroscopy ◽

Principal Component ◽

Data Sets ◽

Prediction Errors ◽

Regression Methods ◽

Multiplicative Scatter Correction ◽

Linearity Improvement

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

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Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Foods ◽

10.3390/foods8100450 ◽

2019 ◽

Vol 8 (10) ◽

pp. 450 ◽

Cited By ~ 4

Author(s):

Annalisa De Girolamo ◽

Marina Cortese ◽

Salvatore Cervellieri ◽

Vincenzo Lippolis ◽

Michelangelo Pascale ◽

...

Keyword(s):

Durum Wheat ◽

Near Infrared ◽

Southern Italy ◽

Geographical Origin ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Model ◽

North Central ◽

Linear Discriminant ◽

The World

Fourier transform near infrared (FT-NIR) spectroscopy, in combination with principal component-linear discriminant analysis (PC-LDA), was used for tracing the geographical origin of durum wheat samples. The classification model PC-LDA was applied to discriminate durum wheat samples originating from Northern, Central, and Southern Italy (n = 181), and to differentiate Italian durum wheat samples from those cultivated in other countries across the world (n = 134). Developed models were validated on a separated set of wheat samples. Different pre-treatments of spectral data and different spectral regions were selected and compared in terms of overall discrimination (OD) rates obtained in validation. The LDA models were able to correctly discriminate durum Italian wheat samples according to their geographical origin (i.e., North, Central, and South) with OD rates of up of 96.7%. Better results were obtained when LDA models were applied to the discrimination of Italian durum wheat samples from those originating from other countries across the world, having OD rates of up to 100%. The excellent results obtained herein clearly indicate the potential of FT-NIR spectroscopy to be used for the discrimination of durum wheat samples according to their geographical origin.

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Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools

Foods ◽

10.3390/foods9111551 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1551

Author(s):

Annalisa De Girolamo ◽

Salvatore Cervellieri ◽

Erminia Mancini ◽

Michelangelo Pascale ◽

Antonio Francesco Logrieco ◽

...

Keyword(s):

Durum Wheat ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Linear Discriminant ◽

Production And Consumption ◽

High Prediction ◽

Validation Set ◽

Performance Results ◽

Prediction Capability

Italy is the country with the largest durum wheat pasta production and consumption. The mandatory labelling for pasta indicating the country of origin of wheat has made consumers more aware about the consumed pasta products and is influencing their choice towards 100% Italian wheat pasta. This aspect highlights the need to promote the use of domestic wheat as well as to develop rapid methodologies for the authentication of pasta. A rapid, inexpensive, and easy-to-use method based on infrared spectroscopy was developed and validated for authenticating pasta made with 100% Italian durum wheat. The study was conducted on pasta marketed in Italy and made with durum wheat cultivated in Italy (n = 176 samples) and on pasta made with mixtures of wheat cultivated in Italy and/or abroad (n = 185 samples). Pasta samples were analyzed by Fourier transform-near infrared (FT-NIR) spectroscopy coupled with supervised classification models. The good performance results of the validation set (sensitivity of 95%, specificity and accuracy of 94%) obtained using principal component-linear discriminant analysis (PC-LDA) clearly demonstrated the high prediction capability of this method and its suitability for authenticating 100% Italian durum wheat pasta. This output is of great interest for both producers of Italian pasta pointing toward authentication purposes of their products and consumer associations aimed to preserve and promote the typicity of Italian products.

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Unlocking the Secrets of Terminalia Kernels Using Near-Infrared Spectroscopy

Applied Spectroscopy ◽

10.1177/0003702821992136 ◽

2021 ◽

pp. 000370282199213

Author(s):

Eshetu Bobasa ◽

Michael Netzel ◽

Anh Dao Thi Phan ◽

Heather Smyth ◽

Yasmina Sultanbawa ◽

...

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Near Infrared Reflectance ◽

Classification Rate ◽

Potential Health ◽

Wide Range ◽

Fruit Samples ◽

The Fourier Transform ◽

Kakadu Plum

In recent years, the native food industry in Australia has increased in both value and volume due to the discovery of a wide range of phytochemicals (e.g., vitamin C, polyphenols) that have potential health benefits. Thus, plant organs and tissues of these native plants are used in a wide range of applications. In particular, the kernel of a native plum, the Kakadu plum ( Terminalia ferdinandiana, Combretaceae) is considered to be rich in lipids and other phytochemical compounds. The aim of this study was to evaluate the use of NIR spectroscopy to analyze and characterize kernel samples and tissues of wild harvest fruit samples. The Fourier transform near-infrared reflectance spectra of cracked kernels, seeds cover tissues, and dry powder Kakadu plum kernels were acquired. Both principal component analysis and partial least squares discriminant analysis were used to analyze and interpret the spectral data. A correct classification rate of 93%, 86%, and 80% was achieved for the identification of kernel provenance using all tissues, seed coats, and the whole nuts, respectively. The results of this study reported for the first time the analysis of Kakadu plum kernels and their tissues using NIR spectroscopy.

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Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits

Journal of Spectroscopy ◽

10.1155/2019/5975461 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Charles L. Y. Amuah ◽

Ernest Teye ◽

Francis Padi Lamptey ◽

Kwasi Nyandey ◽

Jerry Opoku-Ansah ◽

...

Keyword(s):

Near Infrared ◽

Nearest Neighbor ◽

Multivariate Calibration ◽

Principal Component ◽

Quality Parameters ◽

Soluble Solids ◽

Least Squares Regression ◽

Total Soluble Solids ◽

Linear Discriminant ◽

Identification Rate

The potential of predicting maturity using total soluble solids (TSS) and identifying organic from inorganic pineapple fruits based on near-infrared (NIR) spectra fingerprints would be beneficial to farmers and consumers alike. In this study, a portable NIR spectrometer and chemometric techniques were combined to simultaneously identify organically produced pineapple fruits from conventionally produced ones (thus organic and inorganic) and also predict total soluble solids. A total of 90 intact pineapple fruits were scanned with the NIR spectrometer while a digital refractometer was used to measure TSS from extracted pineapple juice. After attempting several preprocessing techniques, multivariate calibration models were built using principal component analysis (PCA), K-nearest neighbor (KNN), and linear discriminant analysis (LDA) to identify the classes (organic and conventional pineapple fruits) while partial least squares regression (PLSR) method was used to determine TSS of the fruits. Among the identification techniques, the MSC-PCA-LDA model accurately identified organic from conventionally produced fruits at 100% identification rate. For quantification of TSS, the MSC-PLSR model gave Rp = 0.851 and RMSEC = 0.950 °Brix, and Rc = 0.854 and RMSEP = 0.842 °Brix at 5 principal components in the calibration set and prediction set, respectively. The results generally indicated that portable NIR spectrometer coupled with the appropriate chemometric tools could be employed for rapid nondestructive examination of pineapple quality and also to detect pineapple fraud due to mislabeling of conventionally produced fruits as organic ones. This would be helpful to farmers, consumers, and quality control officers.

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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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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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