Process Characterization of the Transesterification of Rapeseed Oil to Biodiesel Using Design of Experiments and Infrared Spectroscopy

For optimization of production processes and product quality,often knowledge of the factors influencing the process outcome is compulsory.Thus,process analytical technology(PAT)that allows deeper insight into the process and results in a mathematical description of the process behavior as a simple function based on the most important process factors can help to achieve higher production efficiency and quality.The present study aims at characterizing a well-known industrial process,the transesterification reaction of rapeseed oil with methanol to produce fatty acid methyl esters(FAME)for usage as biodiesel in a continuous micro reactor set-up.To this end,a design of experiment approach is applied,where the effects of two process factors,the molar ratio and the total flow rate of the reactants,are investigated.The optimized process target response is the FAME mass fraction in the purified nonpolar phase of the product as a measure of reaction yield.The quantification is performed using attenuated total reflection infrared spectroscopy in combination with partial least squares regression.The data retrieved during the conduction of the DoE experimental plan were used for statistical analysis.A non-linear model indicating a synergistic interaction between the studied factors describes the reactor behavior with a high coefficient of determination(R^(2))of 0.9608.Thus,we applied a PAT approach to generate further insight into this established industrial process.

A Comparison of CNN and PLSR for Glucose Monitoring Using Mid-Infrared Absorption Spectroscopy

With the development of mid-infrared (MIR) photoelectric devices, mid-infrared spectroscopy has become one of the important methods for non-invasive detection of blood glucose. The mid-infrared region (4000 - 400 cm-1) has the well-known fingerprint region (1200 - 800 cm-1) of glucose, which has clearer characteristic absorption peaks and better specificity. There is a lot of molecular information about glucose in the MIR. The non-invasive detection of blood glucose by mid-infrared spectroscopy needs to achieve certain accuracy, and the quantitative model is an important factor affecting the accuracy of glucose detection. In this paper, the samples of imitation solution containing only glucose and the samples of imitation mixed solution are taken as the research objects, and the mid-infrared spectral data of the samples are collected. The full spectrum partial least squares Regression (PLSR) model, SNV + Ctr-PLSR model, MSC + Ctr-PLSR model, and convolutional neural networks (CNN) model of 3000 - 900 cm-1 band were constructed. Full spectrum PLS model and CNN model of 1200 - 900 cm-1 band were constructed. The experimental results show that the optimal model of the two bands is CNN, then the correlation coefficient of prediction set (Rp) of 3000 - 900 cm-1 band is 0.95, and the root mean square error of pre-diction set (RMSEP) value is 22.10. The Rp of 1200 - 900 cm-1 band is 0.95, and the RMSEP value is 22.54. The research results show that CNN is a promising method, which has higher accuracy than PLSR, and is especially suitable for modeling human complex environment. In addition, the study provides a theoretical and practical basis for CNN in feature selection and model interpretation.

Quantitative analysis of ammonium salts in coking industrial liquid waste treatment process based on Raman spectroscopy

Quantitative analysis of ammonium salts in the process of coking industrial liquid waste treatment is successfully performed based on a compact Raman spectrometer combined with partial least square(PLS) method. Two main components(NH_4SCN and(NH_4)_2S_2O_3) of the industrial mixture are investigated. During the data preprocessing, wavelet denoising and an internal standard normalization method are employed to improve the predicting ability of PLS models. Moreover,the PLS models with different characteristic bands for each component are studied to choose a best resolution. The internal and external calibration results of the validated model show a mass percentage error below 1% for both components.Finally, the repeatabilities and reproducibilities of Raman and reference titration measurements are also discussed.

The use of milk Fourier transform midinfrared spectra and milk yield to estimate heat production as a measure of efficiency of dairy cows

Background:Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions,fermentation gases and heat.Heat production may differ among dairy cows despite comparable milk yield and body weight.Therefore,heat production can be considered an indicator of metabolic efficiency and directly measured in respiration chambers.The latter is an accurate but time-consuming technique.In contrast,milk Fourier transform mid-infrared(FTIR)spectroscopy is an inexpensive high-throughput method and used to estimate different physiological traits in cows.Thus,this study aimed to develop a heat production prediction model using heat production measurements in respiration chambers,milk FTIR spectra and milk yield measurements from dairy cows.Methods:Heat production was computed based on the animal’s consumed oxygen,and produced carbon dioxide and methane in respiration chambers.Heat production data included 16824-h-observations from 64 German Holstein and 20 dual-purpose Simmental cows.Animals were milked twice daily at 07:00 and 16:30 h in the respiration chambers.Milk yield was determined to predict heat production using a linear regression.Milk samples were collected from each milking and FTIR spectra were obtained with MilkoScan FT 6000.The average or milk yield-weighted average of the absorption spectra from the morning and afternoon milking were calculated to obtain a computed spectrum.A total of 288 wavenumbers per spectrum and the corresponding milk yield were used to develop the heat production model using partial least squares(PLS)regression.Results:Measured heat production of studied animals ranged between 712 and 1470 kJ/kg BW0.75.The coefficient of determination for the linear regression between milk yield and heat production was 0.46,whereas it was 0.23 for the FTIR spectra-based PLS model.The PLS prediction model using weighted average spectra and milk yield resulted in a cross-validation variance of 57%and a root mean square error of prediction of 86.5 kJ/kg BW0.75.The ratio of performance to deviation(RPD)was 1.56.Conclusion:The PLS model using weighted average FTIR spectra and milk yield has higher potential to predict heat production of dairy cows than models applying FTIR spectra or milk yield only.

Milk Production of Sarda Suckler Cows with Different Calving Period

The study explored the relationship between the performance of calves and calving season in a Mediterranean rangeland-based beef livestock system.Twenty multiparous Sarda cows,grazing on a natural pasture,with two distinct calving periods(group A,11 animals,calving date 15/10/2016±16(means±s.d.),and group W,nine animals,calving date 26/01/2017±11)were used.Meteorological data,herbage quality,daily milk yield(DMY),total milk yield(TMY),body weight(BW)of cows and calf,body-weight daily gain(ADG)of calves,body condition score(BCS)and calving interval(CI)of cows were assessed.A mixed-effects model was used to DMY and ADG data while TMY,BCS,weaning weight(WW)and CI data were analyzed by a linear model.The most determining factors in the DMY and ADG were detected by means of partial least square regression(PLSR)procedure.Group W showed higher DMY(6.5±0.3 kg/d vs.4.5±0.3 kg/d,p<0.001)and TMY(1,189±70 kg vs.830±60 kg,p=0.002)than Group A,but this did not result in a greater ADG of calves(Group A:0.83±0.04 kg/d/animal and Group W:0.99±0.09 kg/d/animal,p-value not significant)or WW when adjusted for their age(Group A:216±14 kg/animal and Group W:250±22 kg/animal,p-value not significant).In contrast,the WW actually measured were higher in Group A than in Group W(257±7 kg vs.175±8 kg,p<0.001).The Group W cows experienced a minor CI than Group A cows(288±13 d vs.320±8 d,p=0.04).The results of PLSR suggest that the factors with utmost importance for both DMY and ADG were the age and the body-weight of cows,highlighting the excellent maternal ability of Sarda breed and its good adaptation to environment.

Ultrasonic concentration measurement of citrus pectin aqueous solutions using PC and PLS regression

This work demonstrated the use of multivariate statistical techniques called principal component(PC)and partial least squares(PLS)to extract the acoustic features of citrus pectin water solution.The concentration of citrus pectin water solution was predicted by PC and PLS regression method using the spectra of ultrasound pulse echoes travelling through mixtures.The values of root mean square error of validation(RMSEV)were 0.0675 g/100 g and 0.0662 g/100 g for PC and PLS regression model,respectively.Since the response variable was taken into account,PLS regression model was more accurate than PC regression model.Also,a method for temperature compensation was proposed to correct the impact of temperature variation on analyzed data.The proposed methods for pectin concentration measurement are easily adaptable to similar applications using existing hardware.

Rapid detection of aflatoxin B_(1) in paddy rice as analytical quality assessment by near infrared spectroscopy

A rapid identification method for aflatoxin B_(1) in paddy rice samples was developed by using near infrared spectroscopy under a wavelength range of 1000-2500 nm.Eighty paddy rice samples were collected from both natural and artificial infection with aflatoxin B_(1) to build the calibration models based on the partial least square regression method.The best predictive model to detect aflatoxin B_(1) in paddy rice was obtained using standard normal variate detrending spectra,with a correlation of 0.850,and a standard error of prediction of 3.211%.Therefore,the result showed that near infrared spectroscopy could be a useful instrumental method for determining aflatoxin B_(1) in paddy rice.The near infrared spectroscopy methodology can be applied to the monitoring of aflatoxin fungal contamination in postharvest paddy rice during storage and may become a powerful tool for the safety of grain and grain products.

LW-NIR hyperspectral imaging for rapid prediction of TVC in chicken flesh

Total viable count(TVC)is often used as an important indicator for chicken freshness evaluation.In this study,112 fresh chicken flesh samples were acquired after slaughtered and their hyperspectral images were collected in the LW-NIR(900-1700 nm)range.The full LW-NIR spectra(486 wavebands)within the images were extracted and applied to related to reference TVC values measured in different storage periods,using partial least squares regression(PLSR)algorithm,resulting in high correlation coefficients(R)and low root mean square errors(RMSE),for either raw spectra or pretreatment spectra.By using regression coefficients(RC)method,20,18,17 and 20 optimal wavebands were respectively selected from raw spectra,baseline correction(BC)spectra,Savitzky-Golay convolution smoothing(SGCS)spectra and standard normal variate(SNV)spectra and applied for the optimization of original full waveband PLSR model.By comparison,RC-PLSR model based on the SGCS spectra showed a better performance in TVC prediction with RC of 0.98 and RMSEC of 0.35 log10 CFU/g in calibration set,and RP of 0.98 and RMSEP of 0.44 log10 CFU/g in prediction set.At last,by transferring the best RC-PLSR model,the dynamic TVC change during the storage was visualized by color maps to indicate the TVC spoilage degree.The overall study revealed that LW-NIR hyperspectral imaging combined with PLSR could be used to predict the freshness of chicken flesh.

Winter wheat biomass estimation based on canopy spectra

The winter wheat aboveground biomass is an important agronomic parameter to estimate the growth status,and evaluate the yield and quality.Spectrum technique provides a nondestructive and fast method for estimating the winter wheat biomass.In order to find the optimum model by analyzing the wheat canopy spectral characteristic during the whole growth period,field trails were conducted at the National Demonstration Base of Precision Agriculture in Beijing Xiaotangshan town.A portable spectrometer(200-1100 nm)was used to collect the wheat canopy spectra of different varieties at the different growth stages(green stage,jointing stage,booting stage,heading stage and filling stage),clipping the winter wheat at ground level at the same time.Regression and correlation analysis were used to establish the winter wheat biomass estimation models in this study.The results showed that the biggest different bands of the winter wheat canopy spectral reflection curves mainly lied along the blue and near-infrared bands.The spectral reflectance at 678 nm in the visible light range had the best correlation with the biomass(correlation=0.724).The monadic regression analysis,the multiple regression analysis and the partial least squares regression analysis were applied to establish the biomass estimation models,among which the partial least squares regression(PLS)model had higher modeling precision.The R2 of the calibration and validation were 0.916 and 0.911,respectively.The root-mean-square error(RMSE)of the calibration and validation were 0.090 kg and 0.094 kg(Sample area 50 cm×60 cm).The results indicated that the PLS model(400-1000 nm)could fully estimate the aboveground biomass in the whole growth period of wheat with a better measurement accuracy.