scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

scholarly journals Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

2020 ◽  
pp. 23
Author(s):  
Anggita Rosiana Putri ◽  
Abdul Rohman ◽  
Sugeng Riyanto ◽  
Widiastuti Setyaningsih
Keyword(s):  
Root Mean Square Error ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

scholarly journals Rapid Analysis of Alcohol Content During the Green Jujube Wine Fermentation by FT-NIR

2020 ◽  
Vol 145 ◽  
pp. 01037
Author(s):  
Guifeng Li ◽  
Ni Yan ◽  
Lu Yuan ◽  
Jianhu Wu ◽  
Junjie Du ◽  
...  
Keyword(s):  
Latent Variables ◽  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Quantitative Model ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Alcohol Content ◽  
Mean Square Errors

The near-infrared (NIR) spectroscopy combined with partial least square regression (PLS) were applied for the prediction of the alcohol content of jujube wine. The NIR spectroscopy was used to collect the spectral data of the jujube wine samples during fermentation and the data were used to establish the quantitative model of alcohol content to achieve rapid on-line detection. The NIR spectroscopy in the range of 950 to 1650 nm from jujube wine were collected and pre-treated by MSC (Multiplicative Scatter Correction) and FD (First Derivative). The alcohol content was measured with alcohol meter. Spectral wavelength selection and latent variables were optimized for the lowest root mean square errors. The results show that the FD - PLS model, which yielded R2 of 0.9246 and RMSEC of 0.6572, is superior to the MSC- PLS model. Results confirmed that NIR spectroscopy is a promising technique for routine assessment of alcohol content of jujube wine and is a viable and advantageous alternative to the chemical procedures involving laborious extractions. The feasibility of the method was thus verified.

scholarly journals APPLICATION OF FTIR SPECTRA COMBINED WITH CHEMOMETRICS FOR ANALYSIS OF CANDLENUT OIL ADULTERATION

2018 ◽  
Vol 10 (5) ◽  
pp. 54
Author(s):  
Fitri Yuliani ◽  
Sugeng Riyanto ◽  
Abdul Rohman
Keyword(s):  
Root Mean Square Error ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Corn Oil ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Objective: The aim of this study was to use FTIR spectroscopy in combination with chemometrics techniques for quantification and classification of candlenut oil (CnO) from oil adulterants, namely sunflower oil (SFO), soybean oil (SyO), and corn oil (CO).Methods: The spectra of all samples were scanned using Fourier Transform Infrared (FTIR) Spectrophotometer using attenuated total reflectance (ATR) as sampling technique at mid infrared region (4000-650 cm-1). Multivariate calibrations of principle component regression (PCR) and partial least square regression (PLSR) were used for quantitative models to predict the levels of CnO in the binary mixtures with SFO, SyO, and CO.Results: The results showed that CnO in SFO was best quantified using PCR at wavenumbers region of 3100-2800 cm-1. Quantitative analysis of CnO in SyO was carried out using PLSR with normal spectra mode using combined wavenumbers of 1765-1625 and 839-663 cm-1, while CnO in CO was analyzed quantitatively using normal spectra at wavenumbers of 970-857 cm-1. The coefficient of determination (R2) obtained were>0.99 with low values of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). The results of discriminant analysis revealed that authentic CnO can be discriminated from CnO adulterated with SFO, SyO and CO using selected wavenumbers.Conclusion: FTIR spectroscopy combined with chemometrics could be used as rapid and reliable method for authentication of candlenut oil (CnO) adulterated with other oils.

scholarly journals Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

Foods ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 500
Author(s):  
Huihui Wang ◽  
Kunlun Wang ◽  
Xinyu Zhu ◽  
Peng Zhang ◽  
Jixin Yang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Hyperspectral Data ◽  
Correlation Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400–1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky–Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (RC2) = 96.23%, the coefficient of determination in prediction set (RP2) = 95.55%, root mean square error by calibration (RMSEC) = 6.20%, the root mean square error by prediction (RMSEP)= 7.54%, and the relative percent deviation (RPD) = 3.98), belly (RC2 = 93.44%, RP2 = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (RC2 = 95.34%, RP2 = 93.71%, RMSEC = 6.66%, RMSEP = 8.37%, and RPD = 3.42) regions, respectively. It can be seen that PLSR integrated with specific pretreatment and dimension reduction methods had great potential for scaling rate detection in different carp regions. These results confirmed the possibility of using hyperspectral technology in nondestructive and convenient detection of the scaling rate of carp.

Measurement of the Irganox Content in Polypropylene Polymers during Extrusion

2005 ◽  
Vol 13 (3) ◽  
pp. 147-154 ◽  
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.

Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

1995 ◽  
Vol 78 (3) ◽  
pp. 802-806 ◽  
Author(s):  
José Louis Rodriguez-Otero ◽  
Maria Hermida ◽  
Alberto Cepeda
Keyword(s):  
Near Infrared ◽  
Independent Set ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sample Treatment ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Total Solids

Abstract Near-infrared reflectance (NIR) spectroscopy was used to analyze fat, protein, and total solids in cheese without any sample treatment. A set of 92 samples of cow’s milk cheese was used for instrument calibration by principal components analysis and modified partial least-square regression. The following statistical values were obtained: standard error of calibration (SEC) = 0.388 and squared correlation coefficient (R2) = 0.99 for fat, SEC = 0.397 and R2 = 0.98 for protein, and SEC = 0.412 and R2 = 0.99 for total solids. To validate the calibration, an independent set of 25 cheese samples of the same type was used. Standard errors of validation were 0.47,0.50, and 0.61 for fat, protein, and total solids, respectively, and hf for the regression of measurements by reference methods versus measurements by NIR spectroscopy was 0.98 for the 3 components.

scholarly journals Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6745
Author(s):  
Rebecca-Jo Vestergaard ◽  
Hiteshkumar Bhogilal Vasava ◽  
Doug Aspinall ◽  
Songchao Chen ◽  
Adam Gillespie ◽  
...  
Keyword(s):  
Soil Properties ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Fine Sand ◽  
Detailed Comparison ◽  
Coarse Sand ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Modeling Approaches

The absorbance spectra for air-dried and ground soil samples from Ontario, Canada were collected in the visible and near-infrared (VIS-NIR) region from 343 to 2200 nm. The study examined thirteen combination of six preprocessing (1st derivative, 2nd derivative, Savitzky-Golay, Gap, SNV and Detrend) method included in ‘prospectr’ R package along with four modeling approaches: partial least square regression (PLSR), cubist, random forest (RF), and extreme learning machine (ELM) for prediction of the soil organic matter (SOM). The 1st derivative + gap, 2nd derivative + gap and standard normal variance (SNV) were the best preprocessing algorithms. Thus, only these three preprocessing algorithms along with four modeling approaches were used for prediction of soil pH, electrical conductively (EC), %sand, %silt, %clay, %very coarse sand (VCS), %coarse sand (CS), %medium sand (ms) and %fine sand (fs). The results showed that OM, pH, %sand, %silt and %CS were all predicted with confidence (R2 > 0.60) and the combination of 1st derivative + gap and RF gained the best performance. A detailed comparison of the preprocessing and modeling algorithms for various soil properties in this study demonstrate that for better prediction of soil properties using VIS-NIR spectroscopy requires different preprocessing and modeling algorithms. However, in general RF and 1st derivative + gap can be labeled at the best combination of preprocessing and modelling algorithms.

scholarly journals Prediction Is The Determination Of The Levels Of Total Flavonoid Extract Of 70% Ethanol On The Leaves Of Rose Guava (Syzigium Jambos (L.) Alston) By Using A Combination Of FTIR And Chemometrics Plsr (Partial Least Square Regression)

2019 ◽  
Author(s):  
Nur Tsalits Fahman Mughni
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Total Flavonoid ◽  
Partial Least Square Regression ◽  
Mean Square ◽  
Syzygium Jambos ◽  
Least Square Regression

Rose Guava (Syzygium jambos (L.) Alston) is known to have flavonoid compounds. Where flavonoids are natural product compounds that have uses as a treatment. An alternative method used to determine the prediction of total flavonoid levels is a combination of FTIR and Chemometrics (Partial least square regression) through the parameter RMSEC value (Root mean square error of calibration), RMSECV (Root mean square error of validation), PRESS (Predicted residual error sum of squares) and R2. The results of the combination of FTIR and CEMOMETRICS (Partial least square regression) on the prediction of total flavonoid levels can provide a good model with calibration obtained R2 value0.9999, RMSEC 0.0000637 and the results of vaid obtained PRESS value0.19225, R2 0.978 and RMSECV 0.041 . Based on the literature, the model can be said to be good if the RMSEC and RMSECV values are smaller than R2.

Nondestructive Method for Analysis of the Soybean Quality

2012 ◽  
Vol 8 (4) ◽  
Author(s):  
Yan Dong ◽  
Shi You Qu
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Crude Protein ◽  
Near Infrared ◽  
Nondestructive Method ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Pre Treatment

Abstract Fourier transform near infrared (NIR) spectra combined with chemometric methods was proposed to the analysis of the crude protein and fat contents in whole-kernel soybean. The calibration models were established by partial least square. After optimizing spectral pre-treatment, the determination coefficient (R2) of the crude protein and fat were 0.971, 0.970, and root mean square error of calibration (RMSEC) were 0.610, 0.365，respectively. For the prediction samples of the crude protein and fat, root mean square error of prediction (RMSEP) were 0.766, 0.420, respectively. The analytical results showed that NIR spectra had significant potential as a rapid and nondestructive method for the crude protein and fat contents in soybean.

scholarly journals In-situ monitoring of saccharides removal of alcohol precipitation using near-infrared spectroscopy

2018 ◽  
Vol 11 (05) ◽  
pp. 1850027 ◽  
Author(s):  
Hongxia Huang ◽  
Haibin Qu
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Process Analytical Technology ◽  
Nir Spectroscopy ◽  
In Situ Monitoring ◽  
Mean Square ◽  
Model Errors ◽  
Precipitation Processes ◽  
Mean Square Errors

As unsafe components in herbal medicine (HM), saccharides can affect not only the drug appearance and stabilization, but also the drug efficacy and safety. The present study focuses on the in-line monitoring of batch alcohol precipitation processes for saccharide removal using near-infrared (NIR) spectroscopy. NIR spectra in the 4000–10,000-cm[Formula: see text] wavelength range are acquired in situ using a transflectance probe. These directly acquired spectra allow characterization of the dynamic variation tendency of saccharides during alcohol precipitation. Calibration models based on partial least squares (PLS) regression have been developed for the three saccharide impurities, namely glucose, fructose, and sucrose. Model errors are estimated as the root-mean-square errors of cross-validation (RMSECVs) of internal validation and root-mean-square errors of prediction (RMSEPs) of external validation. The RMSECV values of glucose, fructose, and sucrose were 1.150, 1.535, and 3.067[Formula: see text]mg[Formula: see text]mL[Formula: see text], and the RMSEP values were 0.711, 1.547, and 3.740[Formula: see text][Formula: see text], respectively. The correlation coefficients [Formula: see text] between the NIR predictive and the reference measurement values were all above 0.94. Furthermore, NIR predictions based on the constructed models improved our understanding of sugar removal and helped develop a control strategy for alcohol precipitation. The results demonstrate that, as an alternative process analytical technology (PAT) tool for monitoring batch alcohol precipitation processes, NIR spectroscopy is advantageous for both efficient determination of quality characteristics (fast, in situ, and requiring no toxic reagents) and process stability, and evaluating the repeatability.

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