Use of Mean Square Prediction Error Analysis and Reproducibility Measures to Study near Infrared Calibration Equation Performance

1999 ◽  
Vol 7 (3) ◽  
pp. 133-143 ◽  
Author(s):  
M.S. Dhanoa ◽  
S.J. Lister ◽  
J. France ◽  
R.J. Barnes
Keyword(s):  
Error Analysis ◽  
Prediction Error ◽  
Near Infrared ◽  
Mean Square ◽  
Calibration Equation ◽  
Mean Square Prediction Error

scholarly journals SCALING THRESHOLD CHARACTERS

Genetics ◽  
1981 ◽  
Vol 99 (2) ◽  
pp. 357-364 ◽  
Author(s):  
Daniel Gianola ◽  
H W Norton
Keyword(s):  
Prediction Error ◽  
Joint Distribution ◽  
Mean Square ◽  
Scaling Method ◽  
Simple Method ◽  
Normal Variable ◽  
Mean Square Prediction Error ◽  
Genetic Value ◽  
Ordered Categorical ◽  
Threshold Characters

ABSTRACT A simple method of scaling ordered categorical responses having a joint distribution with an underlying normal variable is presented. Scores are developed that maximize heritability of the observed variate and that in the class of scores based on polychotomies: (1) maximize the correlation between score and the underlying genetic value to be predicted, and (2) minimize mean-square prediction error. Several examples suggest little is lost, in terms of heritability, by using equally spaced scores. The proposed scaling method discriminates among candidates for selection that would be tied if equally spaced scores are used and sometimes yields different rankings of candidates.

scholarly journals Body weight prediction using body size measurements in Fleckvieh, Holstein, and Brown Swiss dairy cows in lactation and dry periods

2018 ◽  
Vol 61 (4) ◽  
pp. 413-424
Author(s):  
Leonhard Gruber ◽  
Maria Ledinek ◽  
Franz Steininger ◽  
Birgit Fuerst-Waltl ◽  
Karl Zottl ◽  
...  
Keyword(s):  
Body Weight ◽  
Body Size ◽  
Root Mean Square ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Body Measurements ◽  
Mean Square ◽  
Validation Data ◽  
Brown Swiss ◽  
Mean Square Prediction Error

Abstract. The objective of this study was to predict cows' body weight from body size measurements and other animal data in the lactation and dry periods. During the whole year 2014, 6306 cows (on 167 commercial Austrian dairy farms) were weighed at each routine performance recording and body size measurements like heart girth (HG), belly girth (BG), and body condition score (BCS) were recorded. Data on linear traits like hip width (HW), stature, and body depth were collected three times a year. Cows belonged to the genotypes Fleckvieh (and Red Holstein crosses), Holstein, and Brown Swiss. Body measurements were tested as single predictors and in multiple regressions according to their prediction accuracy and their correlations with body weight. For validation, data sets were split randomly into independent subsets for estimation and validation. Within the prediction models with a single body measurement, heart girth influenced relationship with body weight most, with a lowest root mean square error (RMSE) of 39.0 kg, followed by belly girth (39.3 kg) and hip width (49.9 kg). All other body measurements and BCS resulted in a RMSE of higher than 50.0 kg. The model with heart and belly girth (ModelHG BG) reduced RMSE to 32.5 kg, and adding HW reduced it further to 30.4 kg (ModelHG BG HW). As RMSE and the coefficient of determination improved, genotype-specific regression coefficients for body measurements were introduced in addition to the pooled ones. The most accurate equations, ModelHG BG and ModelHG BG HW, were validated separately for the lactation and dry periods. Root mean square prediction error (RMSPE) ranged between 36.5 and 37.0 kg (ModelHG BG HW, ModelHG BG, lactation) and 39.9 and 41.3 kg (ModelHG BG HW, ModelHG BG, dry period). Accuracy of the predictions was evaluated by decomposing the mean square prediction error (MSPE) into error due to central tendency, error due to regression, and error due to disturbance. On average, 99.6 % of the variance between estimated and observed values was caused by disturbance, meaning that predictions were valid and without systematic estimation error. On the one hand, this indicates that the chosen traits sufficiently depicted factors influencing body weight. On the other hand, the data set was very heterogeneous and large. To ensure high prediction accuracy, it was necessary to include body girth traits for body weight estimation.

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 Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1460
Author(s):  
Jinming Liu ◽  
Changhao Zeng ◽  
Na Wang ◽  
Jianfei Shi ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Square ◽  
Biochemical Methane Potential ◽  
Methane Potential

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

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