scholarly journals IR spectroscopy coupled with chemometrics used as a simple and rapid method to determine the caffeine content of tea products

2021 ◽  
pp. 195-200
Author(s):  
Lestyo Wulandari ◽  
Diana Hanifiyah Sutipno ◽  
Dwi Koko Pratoko
Keyword(s):  
Ir Spectroscopy ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Black Tea ◽  
Calibration Model ◽  
Chemometric Method ◽  
Simple Method ◽  
Caffeine Content ◽  
Squared Error ◽  
Significant Difference

Introduction: Tea is a popular beverage that comes from Camellia sinensis. Tea is generally categorised into four types: black tea, oolong tea, green tea, and white tea. These four types are distinguished based on the presence or absence of a fermentation process during their processing. One of the compounds that play a role in providing freshness to tea is caffeine. Aims: The purpose of this study was to determine the caffeine content in the tea samples that are on the market. Methods: This was done using the near-infrared (NIR)-chemometric method and using the TLC-Densitometry method as a comparison. Infrared (IR) spectroscopy combined with chemometrics has been developed as a simple method to analyse the caffeine content in a tea sample. IR spectra of tea samples were correlated with caffeine content using chemometrics. Results: In this study, the partial least squares (PLS) model of the NIR model that showed the best calibration with r-square was 0.958, and the root mean squared error of calibration (RMSEC) value was 0.070. The PLS calibration model of the NIR models was further used to predict the unknown caffeine content in commercial samples. The significance of the caffeine content that had been measured with NIR and TLC-Densitometry was evaluated using a two paired sample t-test. Conclusion: The caffeine content measured with both methods gave no significant difference.

Measuring The Tensile Strain of Wood By Visible And Near-Infrared Spatially Resolved Spectroscopy

2021 ◽  
Author(s):  
Ma Te ◽  
Tetsuya Inagaki ◽  
Masato Yoshida ◽  
Mayumi Ichino ◽  
Satoru Tsuchikawa
Keyword(s):  
Light Scattering ◽  
Prediction Accuracy ◽  
Tensile Strain ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Calibration Model ◽  
Spatially Resolved ◽  
Squared Error ◽  
Stiffness Evaluation ◽  
Spatially Resolved Spectroscopy

Abstract Wood has various mechanical properties, so stiffness evaluation is critical for quality management. Using conventional strain gauges constantly is high cost, also challenging to measure precious wood materials due to the use of strong adhesive. This study demonstrates the correlation between light scattering changes inside the wood cell walls and tensile strain. A multifiber-based visible-near-infrared (Vis–NIR) spatially resolved spectroscopy (SRS) system was designed to rapidly and conventiently acquire such light scattering changes. For the preliminary experiment, samples with different thicknesses were measured to evaluate the influence of thickness. The differences in Vis–NIR SRS spectral data diminish with an increase in sample thickness, which suggests that the SRS method can successfully measure the whole strain (i.e., surface and inside) of wood samples. Then, for the primary experiment, 18 wood samples with the same thickness (2 mm) were tested to construct a strain calibration model. The prediction accuracy was characterized by a determination coefficient (R2) of 0.86 with a root mean squared error (RMSE) of 297.89 με for five-fold cross-validation; for test validation, The prediction accuracy was characterized by an R2 of 0.82 and an RMSE of 345.44 με.

Calibration Transfer between Miniature Photodiode Array-Based Spectrometers in the near Infrared Assessment of Mandarin Soluble Solids Content

2002 ◽  
Vol 10 (1) ◽  
pp. 27-35 ◽  
Author(s):  
C.V. Greensill ◽  
K.B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Updating ◽  
Soluble Solids ◽  
Soluble Solid Content ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Orthogonal Signal Correction ◽  
Significant Difference ◽  
Photodiode Array ◽  
Direct Standardisation

The transfer of predictive models among photodiode array based, short wave near infrared spectrometers using the same illumination/detection optical geometry has been attempted using various chemometric techniques, including slope and bias correction (SBC), direct standardisation (DS), piecewise direct standardisation (PDS), double window PDS (DWPDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT). Additionally, an interpolation and photometric response correction method, a wavelength selection method and a model updating method were assessed. Calibration transfer was attempted across two populations of mandarin fruit. Model performance was compared in terms of root mean squared error of prediction ( RMSEP), using Fearn's significance testing, for calibration transfer (standardisation) between pairs of spectrometers from a group of four spectrometers. For example, when a calibration model (Root Mean Square Error of Cross-Validation [ RMSECV = 0.26% soluble solid content (SSC)], developed on one spectrometer, was used with spectral data collected on another spectrometer, a poor prediction resulted ( RMSEP = 2.5% SSC). A modified WT method performed significantly better (e.g. RMSEP = 0.25% SSC) than all other standardisation methods (10 of 12 cases), and almost on a par with model updating (MU) (nine cases with no significant difference, one case and two cases significantly better for WT and MU, respectively).

scholarly journals Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

2005 ◽  
Vol 56 (4) ◽  
pp. 417 ◽  
Author(s):  
J. A. Guthrie ◽  
D. J. Reid ◽  
K. B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Updating ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Sample Selection ◽  
Model Performance ◽  
Soluble Solids ◽  
Internal Quality ◽  
Calibration Model ◽  
Validation Population

The robustness of multivariate calibration models, based on near infrared spectroscopy, for the assessment of total soluble solids (TSS) and dry matter (DM) of intact mandarin fruit (Citrus reticulata cv. Imperial) was assessed. TSS calibration model performance was validated in terms of prediction of populations of fruit not in the original population (different harvest days from a single tree, different harvest localities, different harvest seasons). Of these, calibration performance was most affected by validation across seasons (signal to noise statistic on root mean squared error of prediction of 3.8, compared with 20 and 13 for locality and harvest day, respectively). Procedures for sample selection from the validation population for addition to the calibration population (‘model updating’) were considered for both TSS and DM models. Random selection from the validation group worked as well as more sophisticated selection procedures, with approximately 20 samples required. Models that were developed using samples at a range of temperatures were robust in validation for TSS and DM.

scholarly journals SPECTRAL AGROMETEOROLOGICAL MODELING ADAPTED BY MEANS OF SIMPLIFIED TRIANGLE METHOD FOR SOYBEAN IN PARANÁ STATE – BRAZIL

2018 ◽  
Vol 44 ◽  
pp. 154
Author(s):  
Daniela Fernanda da Silva Fuzzo
Keyword(s):  
Mean Squared Error ◽  
High Accuracy ◽  
P Value ◽  
Soybean Yield ◽  
Surface Information ◽  
Squared Error ◽  
Significant Difference ◽  
Weather And Climate ◽  
Triangle Method ◽  
National Systems

Agriculture is an economic activity with high dependence on weather and climate. Special geotechnology and agrometeorological modeling can be used to optimize productivity in regional and national systems, while minimizing costs. The aim was to test the agrometeorological model for estimating crop soybean yield proposed by Doorenbos and Kassam (1979), using only spectral data as input variable in the model obtained by a simplified triangle method applied in Paraná state, for crop years 2002/03 to 2011/12. A high accuracy of the data was found, the model values for the parameter d1 ("d1" modified Willmott) were between 0.8 and 0.95, whereas the root mean squared error showed that there was low variation between 30.81 to 116.88 (kg ha-1) and the p-value was used as the indicator significance of the model at the level of 5%, indicating that there was no statistically significant difference between the estimated and observed data, this means that the average of the data estimated by the model were statistically equal the average of the observed data. Thus, we can say that images of remote sensing can be used as tools in the absence of surface information, in agrometeorological modeling to estimate crop soybean yield.

Visible-near infrared spectroscopy for detection of blood in sheep faeces

2020 ◽  
Vol 28 (5-6) ◽  
pp. 255-266 ◽  
Author(s):  
Elise A Kho ◽  
Jill N Fernandes ◽  
Andrew C Kotze ◽  
Glen P Fox ◽  
Maggy Lord ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Root Mean Squared Error ◽  
Squared Error

Heavy infestations of the blood-sucking gastrointestinal nematodes, Haemonchus contortus can cause severe anaemia in sheep and leakage of blood into the faeces, leading to morbidity and mortality. Early and accurate diagnosis of infections is critical for timely treatment of sheep, minimizing production and sheep welfare impacts. In pursuit of a quick and easy measure of H. contortus infections, we investigated the use of portable visible near infrared spectrometers for detecting the presence of haemoglobin in sheep faeces as an indicator of H. contortus infection. Calibration models built within the 400–600 nm region by partial least square regression resulted in acceptable prediction accuracies (r 2 p > 0.70 and root mean squared error of prediction <2.64 µg Hb mg−1 faeces) for haemoglobin quantification using two spectrometers. The prediction results from support vector machine regression further improved the prediction of haemoglobin in moist sheep faeces (r 2 p > 0.87 and root mean squared error of prediction <2.00 µg haemoglobin mg−1 faeces). Based on a threshold for anthelmintic treatment of 3 µg Hb mg−1 faeces, both the partial least square and support vector machine models showed high sensitivity (89%) and high specificity (>77%). The specificity of the prediction model for detecting haemoglobin in sheep faeces may be improved by adding more variations in faecal composition into the calibration model. Our success in detecting haemoglobin in sheep faeces, following minimal sample preparation, suggests that with further development, vis–near infrared spectroscopy can provide a sensitive and convenient method for on-farm diagnosis of H. contortus infections.

Determination of gel time of prepreg in copper clad laminate industry by near infrared spectroscopy

2020 ◽  
pp. 096703352096379
Author(s):  
Qian-Fa Liu ◽  
Dan Li ◽  
Yao-De Zeng ◽  
Wei-Zhuang Huang
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Manufacturing Process ◽  
Near Infrared ◽  
Reference Method ◽  
Coefficient Of Determination ◽  
Resin Matrix ◽  
Calibration Model ◽  
Gel Time ◽  
Significant Difference

Gel time of prepreg is an important quality determinant in the manufacturing process of Copper Clad Laminate (CCL). Prepreg consists of a glass fiber reinforcement impregnated to a predetermined level with a resin matrix. In this work, near infrared spectroscopy associated with partial least squares (PLS) regression has been applied to analyse the gel time of prepreg samples in the manufacturing process. A total of 250 prepreg samples were randomly divided into a calibration set and a validation prediction set with a ratio of 4:1. The values of Root Mean Square Error of leave-one-out Cross-Validation (RMSECV) and the coefficient of determination (R2) of the calibration model was 2.95 s and 0.92 respectively, with eight PLS factors used. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. The analytical result showed that, NIR spectroscopy was a rapid, nondestructive, and accurate method for real-time prediction of prepreg quality in the CCL manufacturing process.

Optimizing Rice Near-Infrared Models Using Fractional Order Savitzky–Golay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)

2020 ◽  
Vol 74 (4) ◽  
pp. 417-426 ◽  
Author(s):  
Zhenzhen Xia ◽  
Jie Yang ◽  
Jing Wang ◽  
Shengpeng Wang ◽  
Yan Liu
Keyword(s):  
Fractional Order ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Predictive Ability ◽  
Selection Method ◽  
Wavelength Selection ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Accuracy And Stability

Developing a rapid and stable method for analyzing the quality parameters of rice is important. Near-infrared (NIR) spectroscopy combined with chemometric techniques have been used to predict the critical contents of rice and shown its accuracy and stability. To further improve the predictive ability, we combine the derivative method of fractional order Savitzky–Golay derivation (FOSGD) with the wavelength selection method of competitive adaptive reweighted sampling (CARS). Compared with the traditional integer order Savitzky–Golay derivation (IOSGD), the FOSGD could improve the resolution ratio of the raw spectra more effectively. The wavelength selection method, CARS, could further extract the informative variables from the processed spectra. Four key contents of rice samples, including moisture, amylose, chalkiness degree, and gel consistency, were utilized to validate this method. The prediction results indicated that partial least squares (PLS) models optimized with FOSGD-CARS own higher accuracy and stability with smaller the root mean squared error of cross validations (RMSECVs) and root mean squared error of predictions (RMSEPs). The proposed method is convenient and provides a practical alternative for rice analysis.

scholarly journals Analysis of alternative models treating herd × year effects as fixed or random

2011 ◽  
Vol 49 (No. 8) ◽  
pp. 349-356 ◽  
Author(s):  
I. Nagy ◽  
J. Sölkner ◽  
L. Csató ◽  
J. Farkas ◽  
L. Radnóczi
Keyword(s):  
Cross Validation ◽  
Mean Squared Error ◽  
National Database ◽  
Large White ◽  
Squared Error ◽  
Large White Breed ◽  
Significant Difference ◽  
Random Models ◽  
White Breed ◽  
Error Bias

The analysis was conducted of the national database of station tests carried out between May 1996&ndash; February 2001, using the Hungarian Large White breed. Days of test, total amount of consumed feed and valuable cuts were taken into the analysis. Using the method of cross validation, small subsets of the data were excluded and then predicted using the remaining part of the data treating herd &times; year effects either as fixed or as random. The size of the data excluded was 50 or 10 records at a time and the process was repeated 100 or 500 times, respectively. Mean squared error, bias and correlation between the excluded and predicted observations were calculated for all the excluded subsets. There was no significant difference between the fixed and random models but in the case of valuable cuts the random models showed a lower mean squared error and higher correlation between the excluded and predicted observations than the fixed models. &nbsp;

Detection of the Protein Content of Ningxia Tan Sheep Using Hyperspectral Reflectance Imaging

2014 ◽  
Vol 513-517 ◽  
pp. 4235-4238
Author(s):  
Song Lei Wang ◽  
Gui Shan Liu ◽  
Xue Fu Li ◽  
Rui Ming Luo
Keyword(s):  
Protein Content ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Imaging Technique ◽  
Linear Regression Analysis ◽  
Scatter Correction ◽  
Squared Error ◽  
Tan Sheep ◽  
Well Correlation

Near-infrared (NIR) hyperspectral imaging technique (900-1700nm) was evaluated to predict the protein content of Tan sheep. This research adopted NIR hyperspectral imaging to get imaging information of 72 mutton samples, multiplicative scatter correction was used to spectral data preprocessing. The optimal wavelengths were obtained through linear-regression analysis, BP neural network combined with actual measured values were established the prediction model and verified this model. The results showed that the prediction effect of model was very well. Correlation coefficient (Rp) and root mean squared error of prediction (RMSEP) of the protein were 0.87 and 1.19. The results indicated that it is feasible to predict the protein content of Tan sheep for NIR hyperspectral imaging technique.

Importance of Prediction Outlier Diagnostics in Determining a Successful Inter-Vendor Multivariate Calibration Model Transfer

2007 ◽  
Vol 61 (7) ◽  
pp. 747-754 ◽  
Author(s):  
Robert D. Guenard ◽  
Christine M. Wehlburg ◽  
Randy J. Pell ◽  
David M. Haaland
Keyword(s):  
Least Squares ◽  
Outlier Detection ◽  
Prediction Error ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Subset Selection ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Significant Difference ◽  
Pls Method

This paper reports on the transfer of calibration models between Fourier transform near-infrared (FT-NIR) instruments from four different manufacturers. The piecewise direct standardization (PDS) method is compared with the new hybrid calibration method known as prediction augmented classical least squares/partial least squares (PACLS/PLS). The success of a calibration transfer experiment is judged by prediction error and by the number of samples that are flagged as outliers that would not have been flagged as such if a complete recalibration were performed. Prediction results must be acceptable and the outlier diagnostics capabilities must be preserved for the transfer to be deemed successful. Previous studies have measured the success of a calibration transfer method by comparing only the prediction performance (e.g., the root mean square error of prediction, RMSEP). However, our study emphasizes the need to consider outlier detection performance as well. As our study illustrates, the RMSEP values for a calibration transfer can be within acceptable range; however, statistical analysis of the spectral residuals can show that differences in outlier performance can vary significantly between competing transfer methods. There was no statistically significant difference in the prediction error between the PDS and PACLS/PLS methods when the same subset sample selection method was used for both methods. However, the PACLS/PLS method was better at preserving the outlier detection capabilities and therefore was judged to have performed better than the PDS algorithm when transferring calibrations with the use of a subset of samples to define the transfer function. The method of sample subset selection was found to make a significant difference in the calibration transfer results using the PDS algorithm, while the transfer results were less sensitive to subset selection when the PACLS/PLS method was used.

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