Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes

Near-infrared (NIR) spectroscopy has been used to non-destructively and rapidly evaluate the quality of fresh agricultural produce. In this study, two commercially available portable spectrometers (F-750: Felix Instruments, WA, USA; and SCiO: Consumer Physics, Tel Aviv, Israel) were evaluated in the wavelength range between 740 and 1070 nm to non-invasively predict quality attributes, including the dry matter (DM), and total soluble solids (TSS) content of three fresh table grape cultivars (‘Autumn Royal’, ‘Timpson’, and ‘Sweet Scarlet’) and one peach cultivar (‘Cassie’). Prediction models were developed using partial least-square regression (PLSR) to correlate the NIR absorbance spectra with the invasive quality measurements. In regard to grapes, the best DM prediction models yielded an R2 of 0.83 and 0.81, a ratio of standard error of performance to standard deviation (RPD) of 2.35 and 2.29, and a root mean square error of prediction (RMSEP) of 1.40 and 1.44; and the best TSS prediction models generated an R2 of 0.97 and 0.95, an RPD of 5.95 and 4.48, and an RMSEP of 0.53 and 0.70 for the F-750 and SCiO spectrometers, respectively. Overall, PLSR prediction models using both spectrometers were promising to predict table grape quality attributes. Regarding peach, the PLSR prediction models did not perform as well as in grapes, as DM prediction models resulted in an R2 of 0.81 and 0.67, an RPD of 2.24 and 1.74, and an RMSEP of 1.28 and 1.66; and TSS resulted in an R2 of 0.62 and 0.55, an RPD of 1.55 and 1.48, and an RMSEP of 1.19 and 1.25 for the F-750 and SCiO spectrometers, respectively. Overall, the F-750 spectrometer prediction models performed better than those generated by using the SCiO spectrometer data.

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Near-Infrared Technology in Agriculture: Non-Destructive Determination of Inner Quality Parameters in Intact Cocoa Beans

Trends in Sciences ◽

10.48048/tis.2021.31 ◽

2021 ◽

Vol 18 (20) ◽

pp. 31

Author(s):

Zulfahrizal Zulfahrizal ◽

Agus Arip Munawar

Keyword(s):

Prediction Models ◽

Quality Attributes ◽

Quality Parameters ◽

Fat Content ◽

Prediction Performance ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Cocoa Beans ◽

Orthogonal Signal Correction

This present study aimed to apply the near-infrared technology based on reflectance spectroscopy or NIRS in determining 2 main quality attributes on intact cocoa beans namely fat content (FC) and moisture content (MC). Absorbance spectral data, in a wavelength range from 1000 to 2500 nm were acquired and recorded for a total of 110 bulk cocoa bean samples. Meanwhile, actual reference FC and MC were obtained using standard laboratory approaches and Soxhlet and Gravimetry methods. Samples were split onto calibration and validation datasets. The prediction models, used to determine both quality attributes were developed from the calibration dataset using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR). To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) were employed. The results showed that PLSR was better than PCR for both quality parameters prediction. Moreover, spectra corrections enhanced the prediction accuracy and robustness from which OSC was found to be the best correction method for FC and MC determination. The prediction performance using validation dataset generated a correlation coefficient (r), ratio prediction to deviation (RPD), and ratio error to range (RER) indexes for FC were 0.93, 3.16 and 7.12, while for MC prediction, the r coefficient, RPD and RER indexes were 0.96, 3.43 and 9.25, respectively. Based on obtained results, it may conclude that NIRS combined with proper spectra correction and regression approaches can be used to determine inner quality attributes of intact cocoa beans rapidly and simultaneously. HIGHLIGHTS We study and apply NIRS technology as a fast and novel method to predict inner quality parameters of intact cocoa beans in form of moisture and fat content Prediction models, used to determine both quality attributes were developed using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR) To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) The best prediction performance was obtained when the models are constructed using PLSR in combination with OSC correction approach The maximum correlation coefficient (r) and ratio prediction to deviation (RPD) indexes for Fat content were 0.93 and 3.16, while for moisture content prediction, the r coefficient and RPD indexes were 0.96 and 3.43, respectively GRAPHICAL ABSTRACT

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Measurement of the Irganox Content in Polypropylene Polymers during Extrusion

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.467 ◽

2005 ◽

Vol 13 (3) ◽

pp. 147-154 ◽

Cited By ~ 6

Author(s):

Wolfgang Becker ◽

Norbert Eisenreich

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Process Conditions ◽

Transmission Spectra ◽

Mean Square ◽

Score Plot ◽

Least Square Regression

Near infrared spectroscopy was used as an in-line control system for the measurement of polypropylene filled with different amounts of Irganox additives. For this purpose transmission probes were installed in an extruder. The probes can withstand temperatures up to 300°C and pressures up to 60 MPa. Transmission spectra of polypropylene mixed with an Irganox additive were recorded. PCA score plot was carried out revealing the influence of varying conditions for the mixing of the sample preparation. Prediction models were generated with partial least square regression which resulted in a model which estimated Irganox with a coefficient of detremination of 0.984 and a root mean square error of prediction of 0.098%. Furthermore the possibilities for controlling process conditions by measuring transmission at a specific wavelength were shown.

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Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

Frontiers in Chemistry ◽

10.3389/fchem.2020.614718 ◽

2020 ◽

Vol 8 ◽

Author(s):

Roberta Risoluti ◽

Giuseppina Gullifa ◽

Stefano Materazi

Keyword(s):

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

The Novel ◽

Screening Platform ◽

Quality Of Milk ◽

Different Origins ◽

Non Destructive

In this work, an innovative screening platform based on MicroNIR and chemometrics is proposed for the on-site and contactless monitoring of the quality of milk using simultaneous multicomponent analysis. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that allows samples to be processed in a rapid and accurate way and to obtain in a single click a comprehensive characterization of the chemical composition of milk. To optimize the platform, milk specimens with different origins and compositions were considered and prediction models were developed by chemometric analysis of the NIR spectra using Partial Least Square regression algorithms. Once calibrated, the platform was used to predict samples acquired in the market and validation was performed by comparing results of the novel platform with those obtained from the chromatographic analysis. Results demonstrated the ability of the platform to differentiate milk as a function of the distribution of fatty acids, providing a rapid and non-destructive method to assess the quality of milk and to avoid food adulteration.

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Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour

International Agrophysics ◽

10.1515/intag-2016-0025 ◽

2017 ◽

Vol 31 (1) ◽

pp. 35-44 ◽

Cited By ~ 2

Author(s):

Vijayaram Eyarkai Nambi ◽

Kuladaisamy Thangavel ◽

Annamalai Manickavasagan ◽

Sultan Shahir

Keyword(s):

Linear Regression ◽

Principal Component ◽

Textural Properties ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Soluble Solids ◽

Variable Model ◽

Multi Linear Regression ◽

Ripeness Index

Abstract Prediction of ripeness level in climacteric fruits is essential for post-harvest handling. An index capable of predicting ripening level with minimum inputs would be highly beneficial to the handlers, processors and researchers in fruit industry. A study was conducted with Indian mango cultivars to develop a ripeness index and associated model. Changes in physicochemical, colour and textural properties were measured throughout the ripening period and the period was classified into five stages (unripe, early ripe, partially ripe, ripe and over ripe). Multivariate regression techniques like partial least square regression, principal component regression and multi linear regression were compared and evaluated for its prediction. Multi linear regression model with 12 parameters was found more suitable in ripening prediction. Scientific variable reduction method was adopted to simplify the developed model. Better prediction was achieved with either 2 or 3 variables (total soluble solids, colour and acidity). Cross validation was done to increase the robustness and it was found that proposed ripening index was more effective in prediction of ripening stages. Three-variable model would be suitable for commercial applications where reasonable accuracies are sufficient. However, 12-variable model can be used to obtain more precise results in research and development applications.

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Non-Destructive Quality Assessment of Tomato Paste by Using Portable Mid-Infrared Spectroscopy and Multivariate Analysis

Foods ◽

10.3390/foods9091300 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1300

Author(s):

Didem Peren Aykas ◽

Karla Rodrigues Borba ◽

Luis E. Rodriguez-Saona

Keyword(s):

External Validation ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Soluble Solids ◽

Quality Traits ◽

Sampling System ◽

Mid Infrared ◽

Tomato Paste ◽

Ft Ir

This research aims to provide simultaneous predictions of tomato paste’s multiple quality traits without any sample preparation by using a field-deployable portable infrared spectrometer. A total of 1843 tomato paste samples were supplied by four different leading tomato processors in California, USA, over the tomato seasons of 2015, 2016, 2017, and 2019. The reference levels of quality traits including, natural tomato soluble solids (NTSS), pH, Bostwick consistency, titratable acidity (TA), serum viscosity, lycopene, glucose, fructose, ascorbic acid, and citric acid were determined by official methods. A portable FT-IR spectrometer with a triple-reflection diamond ATR sampling system was used to directly collect mid-infrared spectra. The calibration and external validation models were developed by using partial least square regression (PLSR). The evaluation of models was conducted on a randomly selected external validation set. A high correlation (RCV = 0.85–0.99) between the reference values and FT-IR predicted values was observed from PLSR models. The standard errors of prediction were low (SEP = 0.04–35.11), and good predictive performances (RPD = 1.8–7.3) were achieved. Proposed FT-IR technology can be ideal for routine in-plant assessment of the tomato paste quality that would provide the tomato processors with accurate results in shorter time and lower cost.

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Transferability and scaling of soil total carbon prediction models in Florida

10.7287/peerj.preprints.494v1 ◽

2014 ◽

Author(s):

Sabine Grunwald ◽

Congrong Yu ◽

Xiong Xiong

Keyword(s):

Prediction Error ◽

Near Infrared ◽

Prediction Models ◽

Regional Scale ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Total Carbon ◽

Support Vector ◽

Domain Iii

The applicability, transfer, and scalability of visible/near-infrared (VNIR)-derived soil models are still poorly understood. The objectives of this study in Florida, U.S. were to: (i) compare three methods to predict soil total carbon (TC) using five fields (local scale) and a pooled (regional scale) VNIR spectral dataset, (ii) assess the model’s transferability among fields, and (iii) evaluate the up- and down-scaling behavior of TC prediction models. A total of 560 TC-spectral sets were modeled by Partial Least Square Regression (PLSR), Support Vector Machine (SVM), and Random Forest. The transferability and up- and down-scaling of models were limited by the following factors: (i) the spectral data domain, (ii) soil attribute domain, (iii) methods that describe the internal model structure of VNIR-TC relationships, and (iv) environmental domain space of attributes that control soil carbon dynamics. All soil logTC models showed excellent performance based on all three methods with R2 > 0.86, bias < 0.01%, root mean square prediction error (RMSE) = 0.09%, residual predication deviation (RPD) > 2.70% , and ratio of prediction error to inter-quartile range (RPIQ) > 4.54. PLSR performed substantially better than SVM to scale and transfer models. Upscaled soil TC models performed somewhat better in terms of model fit (R2), RPD, and RPIQ, whereas downscaled models showed less bias and smaller RMSE based on PLSR. Given the many factors that can impinge on empirically derived soil spectral prediction models, as demonstrated by this study, more focus on the applicability and scaling of them is needed.

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PSXIII-9 Can we predict feed efficiency using fecal microbiota information?

Journal of Animal Science ◽

10.1093/jas/skz258.937 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 477-477

Author(s):

Mathilde Le Sciellour ◽

Sébastien Dejean ◽

David Renaudeau ◽

Olivier Zemb

Keyword(s):

Feed Efficiency ◽

Prediction Models ◽

Pearson Correlation ◽

Fecal Microbiota ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Real Dataset ◽

The Real ◽

Quantitative Phenotype

Abstract The present study aimed at predicting feed efficiency (FE) based on fecal microbiota, using partial least square regression (PLSR), sparse PLSR, and random forest regression (RF). Fecal samples from 147 Pietrain x (Large White x Landrace) pigs reared in two consecutive batches were collected at 99 days of age. Daily live body weight and feed intake were individually measured in pigs fed ad libitum with a corn soybean diet. The relative abundances of operational taxonomic units (OTU) resulting from fecal 16S rRNA sequencing were used to build the prediction models of FE between 99 and 113 days. From these data, neither PLSR nor RF models have been validated on external datasets. An important over-fitting has been observed in PLSR. With this aim to test the ability of the methods to retrieve information, synthetic OTU were created to fit an artificial Pearson correlation with FE (r² = 0 to 0.9) and were added among the predictors in the dataset. Artificial OTU correlated above 0.37 with FE improved the prediction in sparse PLSR and RF, and reduced the over-fitting. The best predictions were achieved by sparse PLSR. The present study emphasized the ability of sparse PLSR and RF to build valid prediction models of a quantitative phenotype, based on fecal microbiota composition. Since no OTU was correlated above 0.30 with FE in the real dataset, the power of the prediction methods was not enough to extract useful information from the fecal microbiota. The functional redundancy of the microbiota could explain the lack of relevant information in the real dataset to predict pigs’ quantitative phenotype. These results suggest that the best strategy is to run sparse PLSR only if a correlation higher than 0.37 is observed. This study is part of the Feed-a-Gene Project funded from the European Union’s H2020 Program (grant 633531).

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Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS)

PeerJ ◽

10.7717/peerj.8619 ◽

2020 ◽

Vol 8 ◽

pp. e8619

Author(s):

Isadora Kaline Camelo Pires de Oliveira Galdino ◽

Hévila Oliveira Salles ◽

Karina Maria Olbrich dos Santos ◽

Germano Veras ◽

Flávia Carolina Alonso Buriti

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Proximate Composition ◽

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Composition Analysis ◽

Reference Methods

Background In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for its execution. Methods Goat whey samples produced in winter and summer were submitted to proximate composition analysis (moisture, total solids, ashes, proteins, fat and carbohydrates by difference) using reference methods and near infrared spectroscopy (NIRS). The spectral data was preprocessed by baseline correction and the Savitzky–Golay derivative. The models were built using Partial Least Square Regression (PLSR) with raw and preprocessed data for each dependent variable (proximate composition parameter). Results The average whey composition values obtained using the referenced methods were in accordance with the consulted literature. The composition did not differ significantly (p > 0.05) between the summer and winter whey samples. The PLSR models were made available using the following figures of merit: coefficients of determination of the calibration and prediction models (R2cal and R2pred, respectively) and the Root Mean Squared Error Calibration and Prediction (RMSEC and RMSEP, respectively). The best models used raw data for fat and protein determinations and the values obtained by NIRS for both parameters were consistent with their referenced methods. Consequently, NIRS can be used to determine fat and protein in goat whey.

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Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy

Animals ◽

10.3390/ani9090640 ◽

2019 ◽

Vol 9 (9) ◽

pp. 640 ◽

Cited By ~ 4

Author(s):

Goi ◽

Manuelian ◽

Currò ◽

Marchi

Keyword(s):

Mineral Content ◽

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Emission Spectrometry ◽

Near Infrared Reflectance ◽

Dog Food

The pet food industry is interested in performing fast analyses to control the nutritional quality of their products. This study assessed the feasibility of near-infrared spectroscopy to predict mineral content in extruded dry dog food. Mineral content in commercial dry dog food samples (n = 119) was quantified by inductively coupled plasma optical emission spectrometry and reflectance spectra (850–2500 nm) captured with FOSS NIRS DS2500 spectrometer. Calibration models were built using modified partial least square regression and leave-one-out cross-validation. The best prediction models were obtained for S (coefficient of determination; R2 = 0.89), K (R2 = 0.85), and Li (R2 = 0.74), followed by P, B, and Sr (R2 = 0.72 each). Only prediction models for S and K were adequate for screening purposes. This study supports that minerals are difficult to determine with NIRS if they are not associated with organic molecules.

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A Comparative Study of PLSR and SVM-R with Various Preprocessing Techniques for the Quantitative Determination of Soluble Solids Content of Hardy Kiwi Fruit by a Portable Vis/NIR Spectrometer

Foods ◽

10.3390/foods9081078 ◽

2020 ◽

Vol 9 (8) ◽

pp. 1078

Author(s):

Shagor Sarkar ◽

Jayanta Kumar Basak ◽

Byeong Eun Moon ◽

Hyeon Tae Kim

Keyword(s):

Support Vector Machine ◽

Correlation Coefficient ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Soluble Solids ◽

Support Vector ◽

Soluble Solids Content ◽

Support Vector Machine Regression ◽

Solids Content

Linear partial least square and non-linear support vector machine regression analysis with various preprocessing techniques and their combinations were used to determine the soluble solids content of hardy kiwi fruits by a handheld, portable near-infrared spectroscopy. Fruits of four species, namely Autumn sense (A), Chungsan (C), Daesung (D), and Green ball (Gb) were collected from five different areas of Gwangyang (G), Muju (M), Suwon (S), Wonju (Q), and Yeongwol (Y) in South Korea. The dataset for calibration and prediction was prepared based on each area, species, and in combination. Half of the dataset of each area, species, and combined dataset was used as calibrated data and the rest was used for model validation. The best prediction correlation coefficient ranges between 0.67 and 0.75, 0.61 and 0.77, and 0.68 for the area, species, combined dataset, respectively using partial least square regression (PLSR) method with different preprocessing techniques. On the other hand, the best correlation coefficient of predictions using the support vector machine regression (SVM-R) algorithm was 0.68 and 0.80, 0.62 and 0.79, and 0.74 for the area, species, and combined dataset, respectively. In most cases, the SVM-R algorithm produced better results with Autoscale preprocessing except G area and species Gb, whereas the PLS algorithm shows a significant difference in calibration and prediction models for different preprocessing techniques. Therefore, the SVM-R method was superior to the PLSR method in predicting soluble solids content of hardy kiwi fruits and non-linear models may be a better alternative to monitor soluble solids content of fruits. The finding of this research can be used as a reference for the prediction of hardy kiwi fruits soluble solids content as well as harvesting time with better prediction models.

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