One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness.The rapid and accurate identification mechanism for lard adulteration in meat produ...One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness.The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary,for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis.Fourier Transform Infrared Spectroscopy(FTIR)is used in this work to identify lard adulteration in cow,lamb,and chicken samples.A simplified extraction method was implied to obtain the lipids from pure and adulterated meat.Adulterated samples were obtained by mixing lard with chicken,lamb,and beef with different concentrations(10%–50%v/v).Principal component analysis(PCA)and partial least square(PLS)were used to develop a calibration model at 800–3500 cm^(−1).Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken,lamb,and beef samples.The corresponding FTIR peaks for the lard have been observed at 1159.6,1743.4,2853.1,and 2922.5 cm−1,which differentiate chicken,lamb,and beef samples.The wavenumbers offer the highest determination coefficient R2 value of 0.846 and lowest root mean square error of calibration(RMSEC)and root mean square error prediction(RMSEP)with an accuracy of 84.6%.Even the tiniest fat adulteration up to 10%can be reliably discovered using this methodology.展开更多
Aims Studies of the climatic responses of plant assemblages via vege-tation-based environmental reconstructions by weighted averag-ing(WA)regression and calibration are a recent development in modern vegetation ecolog...Aims Studies of the climatic responses of plant assemblages via vege-tation-based environmental reconstructions by weighted averag-ing(WA)regression and calibration are a recent development in modern vegetation ecology.However,the performance of this tech-nique for plot-based vegetation datasets has not been rigorously tested.We assess the estimation accuracy of the WA approach by comparing results,mainly the root mean square error of prediction(RMSEP)of WA regressions for six different vegetation datasets(total species,high-frequency species and low-frequency species as both abundance and incidence)each from two sites.Methods Vegetation-inferred environment(plot elevation)calibrated over time is used to quantify the elevational shift in species assemblages.Accuracy of the calibrations is assessed by comparing the linear regression models developed for estimating elevational shifts.The datasets were also used for the backward predictions to check the robustness of the forward predictions.Important Findings WA regression has a fairly high estimation accuracy,especially with species incidence datasets.However,estimation bias at the extremes of the environmental gradient is evident with all datasets.Out of eight sets(each set with a model for total species,low-frequency species and high-frequency species)of WA regression models,the lowest RMSEPs are produced in the four models based on the total species datasets and in three models based on the high-frequency species only.The inferred environment mirrored the estimation pre-cision of the WA regressions,i.e.precise WA regression models pro-duced more accurate calibrated environmental estimates,which,in turn,resulted in regression models with a higher adjusted r^(2) for estimating the elevational shift in the species assemblages.Reliable environmental estimates for plot-based datasets can be achieved by WA regression and calibration,although the edge effect may be evi-dent if species turnover is high along an extensive environmental gradient.Species incidence(0/1)data may improve the estimation accuracy by minimizing any potential census and field estimation errors that are more likely to occur in species abundance datasets.Species data processing cannot guarantee the most reliable WA regression models.Instead,generally optimal estimations can be achieved by using all the species with a consistent taxonomy in the training and reconstruction datasets.展开更多
文摘One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness.The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary,for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis.Fourier Transform Infrared Spectroscopy(FTIR)is used in this work to identify lard adulteration in cow,lamb,and chicken samples.A simplified extraction method was implied to obtain the lipids from pure and adulterated meat.Adulterated samples were obtained by mixing lard with chicken,lamb,and beef with different concentrations(10%–50%v/v).Principal component analysis(PCA)and partial least square(PLS)were used to develop a calibration model at 800–3500 cm^(−1).Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken,lamb,and beef samples.The corresponding FTIR peaks for the lard have been observed at 1159.6,1743.4,2853.1,and 2922.5 cm−1,which differentiate chicken,lamb,and beef samples.The wavenumbers offer the highest determination coefficient R2 value of 0.846 and lowest root mean square error of calibration(RMSEC)and root mean square error prediction(RMSEP)with an accuracy of 84.6%.Even the tiniest fat adulteration up to 10%can be reliably discovered using this methodology.
文摘Aims Studies of the climatic responses of plant assemblages via vege-tation-based environmental reconstructions by weighted averag-ing(WA)regression and calibration are a recent development in modern vegetation ecology.However,the performance of this tech-nique for plot-based vegetation datasets has not been rigorously tested.We assess the estimation accuracy of the WA approach by comparing results,mainly the root mean square error of prediction(RMSEP)of WA regressions for six different vegetation datasets(total species,high-frequency species and low-frequency species as both abundance and incidence)each from two sites.Methods Vegetation-inferred environment(plot elevation)calibrated over time is used to quantify the elevational shift in species assemblages.Accuracy of the calibrations is assessed by comparing the linear regression models developed for estimating elevational shifts.The datasets were also used for the backward predictions to check the robustness of the forward predictions.Important Findings WA regression has a fairly high estimation accuracy,especially with species incidence datasets.However,estimation bias at the extremes of the environmental gradient is evident with all datasets.Out of eight sets(each set with a model for total species,low-frequency species and high-frequency species)of WA regression models,the lowest RMSEPs are produced in the four models based on the total species datasets and in three models based on the high-frequency species only.The inferred environment mirrored the estimation pre-cision of the WA regressions,i.e.precise WA regression models pro-duced more accurate calibrated environmental estimates,which,in turn,resulted in regression models with a higher adjusted r^(2) for estimating the elevational shift in the species assemblages.Reliable environmental estimates for plot-based datasets can be achieved by WA regression and calibration,although the edge effect may be evi-dent if species turnover is high along an extensive environmental gradient.Species incidence(0/1)data may improve the estimation accuracy by minimizing any potential census and field estimation errors that are more likely to occur in species abundance datasets.Species data processing cannot guarantee the most reliable WA regression models.Instead,generally optimal estimations can be achieved by using all the species with a consistent taxonomy in the training and reconstruction datasets.
