Two chemical methods which are commonly used for rice grain freshness determination were investigated for their efficiencies. Method 1 is made of bromothymol blue indicator, and the principle is based on indicator＇s ...Two chemical methods which are commonly used for rice grain freshness determination were investigated for their efficiencies. Method 1 is made of bromothymol blue indicator, and the principle is based on indicator＇s color which is changed in according to pH of the stored rice grains. Method 2 is based on peroxidase activity which deteriorates during storage of rice grains. Both methods were used for determination of fresh-aged rice index of six Thai-rice cultivars, four from non-waxy rice cultivars （KDML 105, Chai Nat 1, Chai Nat 2 and Phitsanulok 2） and two from waxy rice cultivars （San-pah-tawng and RD6）. Rice samples were kept in the forms of paddy and polished rice. Fresh-aged rice indices were determined using both methods every two weeks over the storage period of 24 weeks （six months）. It was found that both methods were capable of detecting fresh-aged rice indices. The color of chemical solutions changed with regards to the age of rice grains and it could be detected spectrophotometrically. Rice grains which have been kept as paddy provided more consistent results. Method 1 is recommended for industrial application as it is simple, efficient and inexpensive.展开更多
[Objectives]To compare the effects of traditional processing and fresh processing on the quality of Polygonatum odoratum decoction piece.[Methods]The effects of fresh processing and traditional processing on the quali...[Objectives]To compare the effects of traditional processing and fresh processing on the quality of Polygonatum odoratum decoction piece.[Methods]The effects of fresh processing and traditional processing on the quality of P.odoratum decoction piece were compared and analyzed with appearance characteristics,total ash content,extract content,total polysaccharides content,and total flavonoids content as the evaluation indexes.[Results]Fresh processing method in different production areas has different effects on P.odoratum decoction piece.P.odoratum was dried in oven of 50℃.When moisture content was 41.44%-59.67%,it was cut.After complete drying at 50℃,the moisture content of dried P.odoratum was 8.94%-9.60%,and ethanol-soluble extract content was 77.29%-78.20%,and water-soluble extract was 77.7%-78.14%.At this time,the appearance characteristics of section of P.odoratum decoction piece were better than that of traditional processing,which was yellowish white.The total polysaccharide content was higher than that of traditional processing,and the content of total flavonoids was statistically significant different from that of traditional processing.[Conclusions]The quality of P.odoratum decoction piece by fresh processing is better than that of the traditional processing,and it is feasible to use fresh processing.展开更多
Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability ...Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability lias emerged as an important research topic to address industry expectations for reducing costs,in particular,maintenance costs.Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning(ML)for better prediction of software maintainability.This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction(SPMP)using ML techniques.This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria:maintainability prediction techniques,validation methods,accuracy criteria,overall accuracy of ML techniques,and the techniques offering the best performance.The review process followed the well-known syslematic review process.The results show that ML techniques are frequently used in predicting maintainability.In particular,artificial neural network(ANN),support vector machine/regression(SVM/R).regression&decision trees(DT),and fuzzy neuro fuzzy(FNF)techniques are more accurate in terms of PRED and MMRE.The N-fold and leave-one-out cross-validation methods,and the MMRE and PRED accuracy criteria are frequently used in empirical studies.In general,ML techniques outperformed non-machine learning techniques,e.g.,regression analysis(RA)techniques,while FNF outperformed SVM/R.DT.and ANN in most experiments.However,while many techniques were reported superior,no specific one can be identified as the best.展开更多
基金financially supported by the Thailand Research Fund(TRF)(Grant No.MRG-WI535S078)
文摘Two chemical methods which are commonly used for rice grain freshness determination were investigated for their efficiencies. Method 1 is made of bromothymol blue indicator, and the principle is based on indicator＇s color which is changed in according to pH of the stored rice grains. Method 2 is based on peroxidase activity which deteriorates during storage of rice grains. Both methods were used for determination of fresh-aged rice index of six Thai-rice cultivars, four from non-waxy rice cultivars （KDML 105, Chai Nat 1, Chai Nat 2 and Phitsanulok 2） and two from waxy rice cultivars （San-pah-tawng and RD6）. Rice samples were kept in the forms of paddy and polished rice. Fresh-aged rice indices were determined using both methods every two weeks over the storage period of 24 weeks （six months）. It was found that both methods were capable of detecting fresh-aged rice indices. The color of chemical solutions changed with regards to the age of rice grains and it could be detected spectrophotometrically. Rice grains which have been kept as paddy provided more consistent results. Method 1 is recommended for industrial application as it is simple, efficient and inexpensive.
基金Supported by Guangxi Science and Technology Major Project(GUIKE AA22096020)Central Guidance for Local Scientific and Technological Development Funds(ZY20230102)+2 种基金Guilin City Science Research and Technology Development Plan Project(20220104-4,20210202-1,2020011203-1,2020011203-2)Open Project of Guangxi Key Laboratory of Tumor Immunology and Microenvironment Regulation(2022KF005)College Students Innovative Entrepreneurial Training Plan Program(202210601015).
文摘[Objectives]To compare the effects of traditional processing and fresh processing on the quality of Polygonatum odoratum decoction piece.[Methods]The effects of fresh processing and traditional processing on the quality of P.odoratum decoction piece were compared and analyzed with appearance characteristics,total ash content,extract content,total polysaccharides content,and total flavonoids content as the evaluation indexes.[Results]Fresh processing method in different production areas has different effects on P.odoratum decoction piece.P.odoratum was dried in oven of 50℃.When moisture content was 41.44%-59.67%,it was cut.After complete drying at 50℃,the moisture content of dried P.odoratum was 8.94%-9.60%,and ethanol-soluble extract content was 77.29%-78.20%,and water-soluble extract was 77.7%-78.14%.At this time,the appearance characteristics of section of P.odoratum decoction piece were better than that of traditional processing,which was yellowish white.The total polysaccharide content was higher than that of traditional processing,and the content of total flavonoids was statistically significant different from that of traditional processing.[Conclusions]The quality of P.odoratum decoction piece by fresh processing is better than that of the traditional processing,and it is feasible to use fresh processing.
文摘Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability lias emerged as an important research topic to address industry expectations for reducing costs,in particular,maintenance costs.Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning(ML)for better prediction of software maintainability.This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction(SPMP)using ML techniques.This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria:maintainability prediction techniques,validation methods,accuracy criteria,overall accuracy of ML techniques,and the techniques offering the best performance.The review process followed the well-known syslematic review process.The results show that ML techniques are frequently used in predicting maintainability.In particular,artificial neural network(ANN),support vector machine/regression(SVM/R).regression&decision trees(DT),and fuzzy neuro fuzzy(FNF)techniques are more accurate in terms of PRED and MMRE.The N-fold and leave-one-out cross-validation methods,and the MMRE and PRED accuracy criteria are frequently used in empirical studies.In general,ML techniques outperformed non-machine learning techniques,e.g.,regression analysis(RA)techniques,while FNF outperformed SVM/R.DT.and ANN in most experiments.However,while many techniques were reported superior,no specific one can be identified as the best.
