The energy value of milk powder is an important indicator of its nutritional value, meaning it is of great significance to explore methods of quickly detecting this energy value. In...
Chemical fertilizers are important for effectively improving soil fertility, promoting crop growth, and increasing grain yield. Therefore, methods that can quickly and accurately measure the amount of fertilizer in the soil should be developed. In this study, 20 groups of soil samples were analyzed using laser-induced breakdown spectroscopy, and partial least squares (PLS) and random forest (RF) models were established. The prediction performances of the models for the chemical fertilizer content and pH were analyzed as well. The experimental results showed that the R2 and root mean square error (RMSE) of the chemical fertilizer content in the soil obtained using the full-spectrum PLS model were .7852 and 2.2700 respectively. The predicted R2 for soil pH was .7290, and RMSE was .2364. At the same time, the full-spectrum RF model showed R2 of .9471 (an increase of 21%) and RMSE of .3021 (a decrease of 87%) for fertilizer content. R2 for the soil pH under the RF model was .9517 (an increase of 31%), whereas RMSE was .0298 (a decrease of 87%). Therefore, the RF model showed better prediction performance than the PLS model. The results of this study show that the combination of laser-induced breakdown spectroscopy with RF algorithm is a feasible method for rapid determination of soil fertilizer content.
Calcium is the main mineral responsible for healthy bone growth in
infants. Laser-induced breakdown spectroscopy (LIBS) was combined with
a variable importance-based long short-term memory (VI-LSTM) for the
quantitative analysis of calcium in infant formula powder. First, the
full spectra were used to establish PLS (partial least squares) and
LSTM models. The R2 and root-mean-square error (RMSE) of the test set (R
P
2 and RMSE
P
) were 0.1460 and 0.0093 in the PLS
method, respectively, and 0.1454 and 0.0091 in the LSTM model,
respectively. To improve the quantitative performance, variable
selection based on variable importance was introduced to evaluate the
contribution of input variables. The variable importance-based PLS
(VI-PLS) model had R
P
2 and RMSE
P
of 0.1454 and 0.0091, respectively,
whereas the VI-LSTM model had R
P
2 and RMSE
P
of 0.9845 and 0.0037, respectively.
Compared with the LSTM model, the number of input variables in the
VI-LSTM model was reduced to 276, R
P
2 was improved by 114.63%, and RMSE
P
was reduced by 46.38%. The mean
relative error of the VI-LSTM model was 3.33%. We confirm the
predictive ability of the VI-LSTM model for the calcium element in
infant formula powder. Thus, combining VI-LSTM modeling and LIBS has
great potential for the quantitative elemental analysis of dairy
products.
Calcium is the main mineral responsible for healthy bone growth in infants. In this study, LIBS was combined with a variable importance-based long short-term memory (VI-LSTM) for the quantitative analysis of calcium in infant formula powder. Firstly, the full spectra were used to establish PLS and LSTM models. The R2 and root-mean-square error (RMSE) of the test set (R2P and RMSEP) were 0.1460 and 0.0093 in the PLS method, respectively, and 0.1454 and 0.0091 in the LSTM model, respectively. To improve the quantitative performance, variable selection based on variable importance was introduced to evaluate the contribution of input variables. The variable importance-based PLS (VI-PLS) model had R2P and RMSEP of 0.1454 and 0.0091, respectively, whereas the VI-LSTM model had R2P and RMSEP of 0.9845 and 0.0037, respectively. Compared with the LSTM model, the number of input variables in the VI-LSTM model was reduced to 276, R2P was improved by 114.63%, and RMSEP was reduced by 46.38%. The mean relative error of the VI-LSTM model was 3.33%. This study confirms the predictive ability of the VI-LSTM model for calcium element in infant formula powder. Thus, combining VI-LSTM modeling and LIBS has great potential for the quantitative elemental analysis of dairy products.
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