水分是柿饼的重要组成成分,也是影响柿饼制作过程的重要因素。利用可见/近红外反射光谱对柿饼制作过程中的水分含量进行检测。首先,获取柿饼在不同加工阶段的可见/近红外反射光谱(400~1000 nm),采用烘干法测定柿饼水分含量。然后,对光...水分是柿饼的重要组成成分,也是影响柿饼制作过程的重要因素。利用可见/近红外反射光谱对柿饼制作过程中的水分含量进行检测。首先,获取柿饼在不同加工阶段的可见/近红外反射光谱(400~1000 nm),采用烘干法测定柿饼水分含量。然后,对光谱进行Mean smoothing(MS)平滑、多元散射校正(MSC)和一阶导数(1-D)预处理。最后,对不同预处理光谱,结合样本水分含量,使用Samples set partitioning based on joint x-y distance(SPXY)方法划分校正集和验证集,基于SPA方法选择特征波长,建立多元线性回归(MLR)预测模型。结果表明,反射光谱经过MS处理后,确定的9个最优波长组合建立水分检测模型的预测结果最好:预测相关系数(Rp)为0.9690,预测标准残差(SEP)为3.4729%,可见/近红外反射光谱技术可以较好地预测柿饼制作过程中的的水分含量。研究可为柿饼加工过程中的品质快速检测提供一定的技术支撑。展开更多
Objectives:The quality of the fruit seriously affects the economic value of the fruit.Fruit quality is related to many ripening parameters,such as soluble solid content(SSC),pH,and firmness(FM),and is a complex proces...Objectives:The quality of the fruit seriously affects the economic value of the fruit.Fruit quality is related to many ripening parameters,such as soluble solid content(SSC),pH,and firmness(FM),and is a complex process.Traditional methods are inefficient,do not guarantee quality,and do not adapt to the current rhythm of the fruit market.In this paper,a was designed and implemented for quality prediction and maturity level classification of Philippine Cavendish bananas.Materials and Methods:The quality changes of bananas in different stages were analyzed.Twelve light intensity reflectance values for each maturity stage were compared to conventionally measured SSC,FM,PH,and color space.Results:Our device can be compared with traditional forms of quality measurement.The experimental results show that the established predictive model with specific preprocessing and modeling algorithms can effectively determine various banana quality parameters(SSC,pH,FM,L^(*),a^(*),and b^(*)).The RPD values of SSC and a^(*)were greater than 3.0,the RPD values of L^(*)and b^(*)were between 2.5 and 3.0,and the pH and FM were between 2.0 and 2.5.In addition,a new banana maturity level classification method(FSC)was proposed,and the results showed that the method could effectively classify the maturity level classes(i.e.four maturity levels)with an accuracy rate of up to 97.5%.Finally,the MLR and FSC models are imported into the MCU to realize the near-range and long-range real-time display of data.Conclusions:These methods can also be applied more broadly to fruit quality detection,providing a basic framework for future research.展开更多
The development and applications of a ruggedized visible to near-infrared （VIS/NIR） spectrometer system capable of measuring fluid spectra in oilfield wellbores are presented. Real-time assessment of formation fluid...The development and applications of a ruggedized visible to near-infrared （VIS/NIR） spectrometer system capable of measuring fluid spectra in oilfield wellbores are presented. Real-time assessment of formation fluid properties penetrated by an oilfield wellbore is critically important for oilfield operating companies to make informed decisions to optimize the development plan of the well and hydrocarbon reservoir. A ruggedized VIS/NIR spectrometer was designed and built to measure and analyze hydrocarbon spectra reliably under the harsh conditions of the oilfield wellbore environment, including temperature up to 175 ~C, pressure up to 170MPa, and severe mechanical shocks and vibrations. The accuracy of hydrocarbon group composition analysis was compared well with gas chromatography results in the laboratory.展开更多
Silica coated multi-wall carbon nanotubes(MWCNTs),silica@MWCNTs and nanocomposites were synthesized by a sol-gel method.By using the synthesized nanocomposites and also CNTs as templates,silica nanotubes(silica-NTs) w...Silica coated multi-wall carbon nanotubes(MWCNTs),silica@MWCNTs and nanocomposites were synthesized by a sol-gel method.By using the synthesized nanocomposites and also CNTs as templates,silica nanotubes(silica-NTs) were prepared.The optical properties of fabricated nanocomposites and nanotubes were characterized by back-scattering micro Raman,UV/Vis/NIR and FT-IR spectra,which show the presence of CNTs structure in the nanocomposites.UV/Vis/NIR and FT-IR spectra also show the presence of silica compounds.The recorded spectra from UV/Vis/NIR and FT-IR also confirm the presence of silica compounds in the nanotubes.The results of FE-SEM imaging data indicate that the synthesized samples are MWCNTs coated uniformly by silica molecules,which act as the template to synthesize silica-NTs.展开更多
Banded iron formations （BIFs） are major rock units having hematite layers intermittent with silica rich layers and formed by sedimentary processes during late Archean to mid Proterozoic time. In terrestrial environm...Banded iron formations （BIFs） are major rock units having hematite layers intermittent with silica rich layers and formed by sedimentary processes during late Archean to mid Proterozoic time. In terrestrial environment, hematite deposits are mainly found associated with banded iron formations. The BIFs in Lake Superior （Canada） and Carajas （Brazil） have been studied by planetary scientists to trace the evolution of hematite deposits on Mars. Hematite deposits are extensively identified in Meridiani region on Mars. Many hypotheses have been proposed to decipher the mechanism for the formation of these deposits. On the basis of geomorphological and mineralogical studies, aqueous environment of deposition is found to be the most supportive mechanism for its secondary iron rich deposits. In the present study, we examined the spectral characteristics of banded iron formations of Joda and Daitari located in Singhbhum craton in eastern India to check its potentiality as an analog to the aqueous/marine environment on Mars. The prominent banding feature of banded iron formations is in the range of few millimeters to few centimeters in thickness. Fe rich bands are darker （gray） in color compared to the light reddish jaspilitic chert bands. Thin quartz veins （〈4 mm） are occasionally observed in the handspecimens of banded iron formations. Spectral investigations have been conducted in VIS/NIR region of electromagnetic spectrum in the laboratory conditions. Optimum absorption bands identified include 0.65, 0.86, 1.4 and 1.9 μm, in which 0.56 and 0.86 μm absorption bands are due to ferric iron and 1.4 and 1,9 μm bands are due to OH/H2O. To validate the mineralogical results obtained from VlS/NIR spectral radiometry, laser Raman and Fourier transform infrared spectroscopic techniques were utilized and the results were found to be similar. Goethite-hematite association in banded iron formation in Singhbhum craton suggests dehydration activity, which has altered the primary iron oxide phases into the secondary iron oxide phases. The optimum bands identified for the minerals using various spectroscopic techniques can be used as reference for similar mineral deposits on any remote area on Earth or on other hydrated planetary surfaces like Mars.展开更多
Nowadays,China stands as the global leader in terms of potato planting area and total potato production.The rapid and nondestructive detection of the potato quality before processing is of great significance in promot...Nowadays,China stands as the global leader in terms of potato planting area and total potato production.The rapid and nondestructive detection of the potato quality before processing is of great significance in promoting rural revitalization and augmenting farmers’income.However,existing potato quality sorting methods are primarily confined to theoretical research,and the market lacks an integrated intelligent detection system.Therefore,there is an urgent need for a post-harvest potato detection method adapted to the actual production needs.The study proposes a potato quality sorting method based on cross-modal technology.First,an industrial camera obtains image information for external quality detection.A model using the YOLOv5s algorithm to detect external green-skinned,germinated,rot and mechanical damage defects.VIS/NIR spectroscopy is used to obtain spectral information for internal quality detection.A convolutional neural network(CNN)algorithm is used to detect internal blackheart disease defects.The mean average precision(mAP)of the external detection model is 0.892 when intersection of union(IoU)=0.5.The accuracy of the internal detection model is 98.2%.The real-time dynamic defect detection rate for the final online detection system is 91.3%,and the average detection time is 350 ms per potato.In contrast to samples collected in an ideal laboratory setting for analysis,the dynamic detection results of this study are more applicable based on a real-time online working environment.It also provides a valuable reference for the subsequent online quality testing of similar agricultural products.展开更多
Multivariate approaches like machine learning are commonly used in estimation of biochemical traits from spectral and color characteristics of foodstuffs and agricultural commodities.In present study,windfall apples o...Multivariate approaches like machine learning are commonly used in estimation of biochemical traits from spectral and color characteristics of foodstuffs and agricultural commodities.In present study,windfall apples of Golden Delicious,Oregon Spur and Granny Smith cultivars were dried in open-sun,controlled greenhouse,microwave oven(200W),hybrid system(100W+60℃),convective dryer(70℃)and freeze-dryer(−55℃).Spectral,chromatic and biochemical characteristics of dried apples were determined and assessed through machine learning algorithms.Total phenolic matter,DPPH(2,2-Diphenyl-1-picrylhydrazyl),FRAP(Ferric Reducing Antioxidant Power)and ascorbic acid content were estimated with the use of five different machine learning algorithms(artificial neural networks,k-nearest neighbor,random forest,gaussian processes and support vector regression).The most successful results were achieved in estimation of total phenolic content(R≥0.85).Additionally,Multilayer Perceptron,Support Vector Regression and Gaussian Processes were identified as the best machine learning algorithms in estimation of biochemical compositions of dried apples.展开更多
文摘水分是柿饼的重要组成成分,也是影响柿饼制作过程的重要因素。利用可见/近红外反射光谱对柿饼制作过程中的水分含量进行检测。首先,获取柿饼在不同加工阶段的可见/近红外反射光谱(400~1000 nm),采用烘干法测定柿饼水分含量。然后,对光谱进行Mean smoothing(MS)平滑、多元散射校正(MSC)和一阶导数(1-D)预处理。最后,对不同预处理光谱,结合样本水分含量,使用Samples set partitioning based on joint x-y distance(SPXY)方法划分校正集和验证集,基于SPA方法选择特征波长,建立多元线性回归(MLR)预测模型。结果表明,反射光谱经过MS处理后,确定的9个最优波长组合建立水分检测模型的预测结果最好:预测相关系数(Rp)为0.9690,预测标准残差(SEP)为3.4729%,可见/近红外反射光谱技术可以较好地预测柿饼制作过程中的的水分含量。研究可为柿饼加工过程中的品质快速检测提供一定的技术支撑。
基金This research is supported by the 2115 Talent Development Program of China Agricultural University,China.
文摘Objectives:The quality of the fruit seriously affects the economic value of the fruit.Fruit quality is related to many ripening parameters,such as soluble solid content(SSC),pH,and firmness(FM),and is a complex process.Traditional methods are inefficient,do not guarantee quality,and do not adapt to the current rhythm of the fruit market.In this paper,a was designed and implemented for quality prediction and maturity level classification of Philippine Cavendish bananas.Materials and Methods:The quality changes of bananas in different stages were analyzed.Twelve light intensity reflectance values for each maturity stage were compared to conventionally measured SSC,FM,PH,and color space.Results:Our device can be compared with traditional forms of quality measurement.The experimental results show that the established predictive model with specific preprocessing and modeling algorithms can effectively determine various banana quality parameters(SSC,pH,FM,L^(*),a^(*),and b^(*)).The RPD values of SSC and a^(*)were greater than 3.0,the RPD values of L^(*)and b^(*)were between 2.5 and 3.0,and the pH and FM were between 2.0 and 2.5.In addition,a new banana maturity level classification method(FSC)was proposed,and the results showed that the method could effectively classify the maturity level classes(i.e.four maturity levels)with an accuracy rate of up to 97.5%.Finally,the MLR and FSC models are imported into the MCU to realize the near-range and long-range real-time display of data.Conclusions:These methods can also be applied more broadly to fruit quality detection,providing a basic framework for future research.
文摘The development and applications of a ruggedized visible to near-infrared （VIS/NIR） spectrometer system capable of measuring fluid spectra in oilfield wellbores are presented. Real-time assessment of formation fluid properties penetrated by an oilfield wellbore is critically important for oilfield operating companies to make informed decisions to optimize the development plan of the well and hydrocarbon reservoir. A ruggedized VIS/NIR spectrometer was designed and built to measure and analyze hydrocarbon spectra reliably under the harsh conditions of the oilfield wellbore environment, including temperature up to 175 ~C, pressure up to 170MPa, and severe mechanical shocks and vibrations. The accuracy of hydrocarbon group composition analysis was compared well with gas chromatography results in the laboratory.
文摘Silica coated multi-wall carbon nanotubes(MWCNTs),silica@MWCNTs and nanocomposites were synthesized by a sol-gel method.By using the synthesized nanocomposites and also CNTs as templates,silica nanotubes(silica-NTs) were prepared.The optical properties of fabricated nanocomposites and nanotubes were characterized by back-scattering micro Raman,UV/Vis/NIR and FT-IR spectra,which show the presence of CNTs structure in the nanocomposites.UV/Vis/NIR and FT-IR spectra also show the presence of silica compounds.The recorded spectra from UV/Vis/NIR and FT-IR also confirm the presence of silica compounds in the nanotubes.The results of FE-SEM imaging data indicate that the synthesized samples are MWCNTs coated uniformly by silica molecules,which act as the template to synthesize silica-NTs.
基金financially supported by Indian Institute of Space Science and Technology(IIST),Thiruvananthapuram
文摘Banded iron formations （BIFs） are major rock units having hematite layers intermittent with silica rich layers and formed by sedimentary processes during late Archean to mid Proterozoic time. In terrestrial environment, hematite deposits are mainly found associated with banded iron formations. The BIFs in Lake Superior （Canada） and Carajas （Brazil） have been studied by planetary scientists to trace the evolution of hematite deposits on Mars. Hematite deposits are extensively identified in Meridiani region on Mars. Many hypotheses have been proposed to decipher the mechanism for the formation of these deposits. On the basis of geomorphological and mineralogical studies, aqueous environment of deposition is found to be the most supportive mechanism for its secondary iron rich deposits. In the present study, we examined the spectral characteristics of banded iron formations of Joda and Daitari located in Singhbhum craton in eastern India to check its potentiality as an analog to the aqueous/marine environment on Mars. The prominent banding feature of banded iron formations is in the range of few millimeters to few centimeters in thickness. Fe rich bands are darker （gray） in color compared to the light reddish jaspilitic chert bands. Thin quartz veins （〈4 mm） are occasionally observed in the handspecimens of banded iron formations. Spectral investigations have been conducted in VIS/NIR region of electromagnetic spectrum in the laboratory conditions. Optimum absorption bands identified include 0.65, 0.86, 1.4 and 1.9 μm, in which 0.56 and 0.86 μm absorption bands are due to ferric iron and 1.4 and 1,9 μm bands are due to OH/H2O. To validate the mineralogical results obtained from VlS/NIR spectral radiometry, laser Raman and Fourier transform infrared spectroscopic techniques were utilized and the results were found to be similar. Goethite-hematite association in banded iron formation in Singhbhum craton suggests dehydration activity, which has altered the primary iron oxide phases into the secondary iron oxide phases. The optimum bands identified for the minerals using various spectroscopic techniques can be used as reference for similar mineral deposits on any remote area on Earth or on other hydrated planetary surfaces like Mars.
基金supported by the Zhejiang Province Key Research and Development Program(Grant No.2021C02011)Zhejiang Province Public Welfare Technology Application Research Project(Grant No.LGN18-F030002)+3 种基金Hangzhou Science and Technology Bureau(Grant No.20201203B116)Program of“Xinmiao”(Potential)Talents in Zhejiang Province(Grant Number:2022R4-07B055)the Graduate Scientific Research Foundation of Hangzhou Dianzi University(Grant No.CXJJ2022177)the Major Science and Technology Projects of Breeding New Varieties of Agriculture in Zhejiang Province(Grant No.2021C02074).
文摘Nowadays,China stands as the global leader in terms of potato planting area and total potato production.The rapid and nondestructive detection of the potato quality before processing is of great significance in promoting rural revitalization and augmenting farmers’income.However,existing potato quality sorting methods are primarily confined to theoretical research,and the market lacks an integrated intelligent detection system.Therefore,there is an urgent need for a post-harvest potato detection method adapted to the actual production needs.The study proposes a potato quality sorting method based on cross-modal technology.First,an industrial camera obtains image information for external quality detection.A model using the YOLOv5s algorithm to detect external green-skinned,germinated,rot and mechanical damage defects.VIS/NIR spectroscopy is used to obtain spectral information for internal quality detection.A convolutional neural network(CNN)algorithm is used to detect internal blackheart disease defects.The mean average precision(mAP)of the external detection model is 0.892 when intersection of union(IoU)=0.5.The accuracy of the internal detection model is 98.2%.The real-time dynamic defect detection rate for the final online detection system is 91.3%,and the average detection time is 350 ms per potato.In contrast to samples collected in an ideal laboratory setting for analysis,the dynamic detection results of this study are more applicable based on a real-time online working environment.It also provides a valuable reference for the subsequent online quality testing of similar agricultural products.
基金grateful to Scientific Research Department of Erciyes University-Turkey for their financial support(Project no:FBA-2020-10157).
文摘Multivariate approaches like machine learning are commonly used in estimation of biochemical traits from spectral and color characteristics of foodstuffs and agricultural commodities.In present study,windfall apples of Golden Delicious,Oregon Spur and Granny Smith cultivars were dried in open-sun,controlled greenhouse,microwave oven(200W),hybrid system(100W+60℃),convective dryer(70℃)and freeze-dryer(−55℃).Spectral,chromatic and biochemical characteristics of dried apples were determined and assessed through machine learning algorithms.Total phenolic matter,DPPH(2,2-Diphenyl-1-picrylhydrazyl),FRAP(Ferric Reducing Antioxidant Power)and ascorbic acid content were estimated with the use of five different machine learning algorithms(artificial neural networks,k-nearest neighbor,random forest,gaussian processes and support vector regression).The most successful results were achieved in estimation of total phenolic content(R≥0.85).Additionally,Multilayer Perceptron,Support Vector Regression and Gaussian Processes were identified as the best machine learning algorithms in estimation of biochemical compositions of dried apples.
