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In the field of Analytical Chemistry and, in particular, whenever a quali-quantitative analysis is required, until a few years ago, reference was made exclusively to instrumental methods (more or less hyphenated) which, once validated, were able to provide the answers to the questions present, even if only in a limited way to analytical targets. Nowadays, the landscape has become considerably complicated (natural adulterants, assessment of geographical origin, sophistication, need for non-destructive analysis, search for often unknown compounds), and new procedures for processing data have greatly increased the potential of analyses that are conducted (even routinely) in the laboratory. In this scenario, chemometrics is master, able to manage and process a huge amount of information based both on data relating only to the analytes of interest, but also by applying “general” procedures to process raw untargeted analysis data. It is within this strand of analysis that many of the works reported in this Special Issue fall. In the succession of works in this printed version, the criterion that guided us was to highlight how—starting exclusively from chromatographic techniques (HPLC and GC) with conventional detectors and moving to exclusively spectroscopic techniques (MS, FT-IR and Raman)—it is possible arrive at extremely powerful coupled techniques and procedures (HPLC and FT-IR) able to meet research needs. Finally, at the end of the printed volume, there are two reviews that surveying the state of the art regarding the assessment of authenticity through qualitative analyses and the application of chemometrics in the pharmaceutical field in the study of forced drug degradation products. From the succession of works (and, above all, from the various application fields) it can immediately be seen how the application of chemometrics and its procedures to both raw and processed data is a powerful means of obtaining robust, reproducible, and predictive information. In this manner, it is possible to create models able to explain and respond to the original problem in a much more detailed way. , and Honghe through Fourier transform mid infrared (FT-MIR) spectra combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. Melucci and collaborators apply chemometric approaches to non-destructive analysis of ATR-FT-IR for the determination of biosilica content. This value was directly evaluated in sediment samples, without any chemical alteration, using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, and the quantification was performed by combining the multivariate standard addition method (MSAM) with the net analyte signal (NAS) procedure to solve the strong matrix effect of sediment samples. Still in the food and food supplements field, Anguebes-Franseschi and collaborators report an article where 10 chemometric models based on Raman spectroscopy were applied to predict the physicochemical properties of honey produced in the state of Campeche, Mexico.

Medicine --- Paris polyphylla Smith var. yunnanensis --- multivariate analysis --- chemometrics --- Fourier transform infrared --- amino acids --- reversed-phase liquid chromatography --- gradient elution --- retention prediction --- artificial neural network --- Macrohyporia cocos --- data fusion --- liquid chromatography --- fourier transform infrared spectroscopy --- partial least squares discriminant analysis --- authentication --- Gastrodia elata tuber --- quality evaluation --- HPLC --- QAMS --- Ranae Oviductus --- identification --- protein --- RP-HPLC --- fingerprint --- fish and seafood --- food authentication --- fingerprinting --- wild and farmed --- geographical origin --- vibrational spectroscopy --- absorption/fluorescence spectroscopy --- nuclear magnetic resonance --- hyperspectral imaging --- saffron --- adulteration --- food authenticity --- gas-chromatography --- eupatorin --- UHPLC-Q-TOF-MS/MS --- metabolism --- in vivo and in vitro --- rat liver microsomes --- rat intestinal flora --- untargeted metabolomics --- PARAFAC2 --- alignment --- gas chromatography–mass spectrometry (GC–MS) --- prostate carcinoma --- forced degradation --- degradation products --- stress test --- diatoms --- biogenic silica --- ATR-FTIR --- NAS --- quality control --- Raman spectroscopy --- honey --- PLS regression models --- physicochemical parameters

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In the field of Analytical Chemistry and, in particular, whenever a quali-quantitative analysis is required, until a few years ago, reference was made exclusively to instrumental methods (more or less hyphenated) which, once validated, were able to provide the answers to the questions present, even if only in a limited way to analytical targets. Nowadays, the landscape has become considerably complicated (natural adulterants, assessment of geographical origin, sophistication, need for non-destructive analysis, search for often unknown compounds), and new procedures for processing data have greatly increased the potential of analyses that are conducted (even routinely) in the laboratory. In this scenario, chemometrics is master, able to manage and process a huge amount of information based both on data relating only to the analytes of interest, but also by applying “general” procedures to process raw untargeted analysis data. It is within this strand of analysis that many of the works reported in this Special Issue fall. In the succession of works in this printed version, the criterion that guided us was to highlight how—starting exclusively from chromatographic techniques (HPLC and GC) with conventional detectors and moving to exclusively spectroscopic techniques (MS, FT-IR and Raman)—it is possible arrive at extremely powerful coupled techniques and procedures (HPLC and FT-IR) able to meet research needs. Finally, at the end of the printed volume, there are two reviews that surveying the state of the art regarding the assessment of authenticity through qualitative analyses and the application of chemometrics in the pharmaceutical field in the study of forced drug degradation products. From the succession of works (and, above all, from the various application fields) it can immediately be seen how the application of chemometrics and its procedures to both raw and processed data is a powerful means of obtaining robust, reproducible, and predictive information. In this manner, it is possible to create models able to explain and respond to the original problem in a much more detailed way. , and Honghe through Fourier transform mid infrared (FT-MIR) spectra combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. Melucci and collaborators apply chemometric approaches to non-destructive analysis of ATR-FT-IR for the determination of biosilica content. This value was directly evaluated in sediment samples, without any chemical alteration, using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, and the quantification was performed by combining the multivariate standard addition method (MSAM) with the net analyte signal (NAS) procedure to solve the strong matrix effect of sediment samples. Still in the food and food supplements field, Anguebes-Franseschi and collaborators report an article where 10 chemometric models based on Raman spectroscopy were applied to predict the physicochemical properties of honey produced in the state of Campeche, Mexico.

Medicine --- Paris polyphylla Smith var. yunnanensis --- multivariate analysis --- chemometrics --- Fourier transform infrared --- amino acids --- reversed-phase liquid chromatography --- gradient elution --- retention prediction --- artificial neural network --- Macrohyporia cocos --- data fusion --- liquid chromatography --- fourier transform infrared spectroscopy --- partial least squares discriminant analysis --- authentication --- Gastrodia elata tuber --- quality evaluation --- HPLC --- QAMS --- Ranae Oviductus --- identification --- protein --- RP-HPLC --- fingerprint --- fish and seafood --- food authentication --- fingerprinting --- wild and farmed --- geographical origin --- vibrational spectroscopy --- absorption/fluorescence spectroscopy --- nuclear magnetic resonance --- hyperspectral imaging --- saffron --- adulteration --- food authenticity --- gas-chromatography --- eupatorin --- UHPLC-Q-TOF-MS/MS --- metabolism --- in vivo and in vitro --- rat liver microsomes --- rat intestinal flora --- untargeted metabolomics --- PARAFAC2 --- alignment --- gas chromatography–mass spectrometry (GC–MS) --- prostate carcinoma --- forced degradation --- degradation products --- stress test --- diatoms --- biogenic silica --- ATR-FTIR --- NAS --- quality control --- Raman spectroscopy --- honey --- PLS regression models --- physicochemical parameters

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Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed.

optical sensor --- spectral analysis --- response surface sampling --- sensor evaluation --- electromagnetic induction --- multivariate water quality parameters --- mandarin orange --- crop inspection platform --- SPA-MLR --- object tracking --- feature selection --- simultaneous measurement --- diseases --- genetic algorithms --- processing of sensed data --- electrochemical sensors --- thermal image --- ECa-directed soil sampling --- handheld --- recognition patterns --- salt concentration --- clover-grass --- bovine embedded hardware --- weed control --- soil --- field crops --- vineyard --- connected dominating set --- water depth sensors --- SS-OCT --- wheat --- striped stem-borer --- silage --- geostatistics --- detection --- NIR hyperspectral imaging --- electronic nose --- machine learning --- virtual organizations of agents --- packing density --- data validation and calibration --- dataset --- Wi-SUN --- temperature sensors --- geoinformatics --- gas sensor --- X-ray fluorescence spectroscopy --- vegetable oil --- photograph-grid method --- Vitis vinifera --- WSN distribution algorithms --- laser-induced breakdown spectroscopy --- irrigation --- quality assessment --- energy efficiency --- wireless sensor network (WSN) --- geo-information --- Fusarium --- texture features --- weeds --- discrimination --- big data --- soil moisture sensors --- meat spoilage --- land cover --- stereo imaging --- near infrared sensors --- biological sensing --- compound sensor --- pest management --- moisture --- plant localization --- heavy metal contamination --- artificial neural networks --- spectral pre-processing --- moisture content --- apparent soil electrical conductivity --- data fusion --- semi-arid regions --- smart irrigation --- back propagation model --- wireless sensor network --- energy balance --- light-beam --- fluorescent measurement --- agriculture --- precision agriculture --- deep learning --- spectroscopy --- hulled barely --- dielectric probe --- RPAS --- water supply network --- rice leaves --- mobile app --- gradient boosted machines --- hyperspectral camera --- one-class --- nitrogen --- LiDAR --- total carbon --- chemometrics analysis --- rice --- agricultural land --- on-line vis-NIR measurement --- CARS --- obstacle detection --- stratification --- neural networks --- regression estimator --- Kinect --- proximity sensing --- distributed systems --- pest --- noninvasive detection --- texture feature --- soil mapping --- classification --- soil salinity --- visible and near-infrared reflectance spectroscopy --- germination --- computer vision --- hyperspectral imaging --- diffusion --- dielectric dispersion --- UAS --- random forests --- case studies --- total nitrogen --- thermal imaging --- cameras --- dry matter composition --- near-infrared --- salt tolerance --- deep convolutional neural networks --- soil type classification --- water management --- preprocessing methods --- wireless sensor networks (WSN) --- remote sensing image classification --- precision plant protection --- radar --- spatial variability --- GF-1 satellite --- plant disease --- naked barley --- leaf area index --- CIE-Lab --- change of support --- radiative transfer model --- 3D reconstruction --- plant phenotyping --- vine --- near infrared --- vegetation indices --- remote sensing --- greenhouse --- time-series data --- scattering --- sensor --- crop area --- speckle --- spatial data --- grapevine breeding --- wide field view --- partial least squares-discriminant analysis --- spiking --- area frame sampling --- chromium content --- machine-learning --- RGB-D sensor --- pest scouting --- PLS --- Capsicum annuum --- spatial-temporal model --- drying temperature --- boron tolerance --- ambient intelligence --- laser wavelength --- fuzzy logic --- dynamic weight --- landslide --- management zones --- real-time processing --- event detection --- crop monitoring --- apple shelf-life --- rice field monitoring --- wireless sensor --- birth sensor --- proximal sensor