Waveband Selection with Equivalent Prediction Performance for FTIR/ATR Spectroscopic Analysis of COD in Sugar Refinery Waste Water

The level of chemical oxygen demand(COD)is an important index to evaluate whether sewage meets the discharge requirements,so corresponding tests should be carried out before discharge.Fourier transform infrared spectroscopy(FTIR)and attenuated total reflectance(ATR)can detect COD in sewage effectively,which has advantages over conventional chemical analysis methods.And the selection of characteristic bands was one of the key links in the application of FTIR/ATR spectroscopy.In this work,based on the moving window partial least-squares(MWPLS)regression to select a characteristic wavelength,a method of equivalent wavelength selection was proposed combining with paired t-test equivalent concept.The results showed that the prediction effect of the selected wavelength was very close to that of the MWPLS method,while the number of wavelength points was much smaller.SEPAve,RP,Ave,SEPStd,and RP,Std which characterized the modeling effect were 26.3 mg L^-1,0.969,3.49 mg L^-1,and 0.006,respectively.The validation effect V-SEP and V-RP were 28.64 mg L^-1 and 0.960,respectively.The selected waveband was between 1809 cm^-1 and 1568 cm^-1.The method was of more reference value for the design of FTIR/ATR spectral instrument for COD detection.

Band Selection Method of Absorption Peak Perturbance for the FTIR/ATR SpectrumAnalysis

The rapid quantification method of human serum glucose was established by using the Fourier transform infrared spectroscopy(FTIR)and attenuated total reflection(ATR).By the subtracted spectra between glucose aqueous solution and de-ionized water,absorption peaks are calculated in fingerprint area.Based on these absorption peaks and multiple linear regression(MLR)model,discrete band selection method of absorption peaks disturbance model(APDM)was developed.5 absorption peaks 1150 cm^-1,1103 cm^-1,1078 cm^-1,1034 cm^-1,991 cm^-1 were found in fingerprint area.Used these absorption peaks to establish absorption peaks disturbance model,the optimal wavelength combinations are 1140 cm^-1,1096 cm^-1,1084 cm^-1,1030 cm^-1,993 cm^-1,the corresponding C-RMSEP and C-RP are 1.164 mmol/L and 0.828 respectively.The results show that the optimal prediction effect of APDM was obviously better than the one of the Partial least squares(PLS)model,and the complexity of the optimal model is reduced greatly also.The results also provide a theoretical basis for design of small and portable human serum glucose spectrometer.

Improve the industrial digital transformation through Industrial Internet platforms

1 Introduction 1.1 Definition and stakeholders of the Industrial Internet The Industrial Internet is a novel network technology system that applies the Internet of Things(IoT)to the manufacturing and industrial sectors.It facilitates the connection of various sensors,devices,systems,factories,and enterprises,enabling the sharing and exchange of data to improve production efficiency,reduce costs,enhance product quality,and enable smart manufacturing.The Industrial Internet serves as a crucial cornerstone and essential support for the Fourth Industrial Revolution,offering specific approaches for industrial digital transformation.Through the comprehensive interconnection of people,machines,and things,the Industrial Internet analyzes various data from intelligent feedback,fostering the development of a new manufacturing and service system.

Paradigm change in Indian agricultural practices using Big Data: Challenges and opportunities from field to plate

Agriculture is the backbone of the Indian Economy.However,statistics show that the rural population and arable land per person is declining.This is an ominous development for a country with a population of more than one billion,with over sixty-six percent living in rural areas.This paper aims to review current studies and research in agriculture,employing the recent practice of Big Data analysis,to address various problems in this sector.To execute this review,this article outline a framework for Big Data analytics in agriculture and present ways in which they can be applied to solve problems in the present agricultural domain.Another goal of this review is to gain insight into state-of-the-art Big Data applications in agriculture and to use a structural approach to identify challenges to be addressed in this area.This review of Big Data applications in the agricultural sector has also revealed several collection and analytics tools that may have implications for the power relationships between farmers and large corporations.