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It is important to detect and distinguish different spices because spices are widely used around the world. In this study, wormwood, artemisia annua, lemongrass and clove are taken as examples. First, laser-induced breakdown spectroscopy (LIBS) is applied to detect and analyze the ash of different spice samples in situ. In the spectra of the ash of different samples, some characteristic lines of metal elements are observed, such as Ca, Na, Mg, K, and so on. By comparing the spectra of the ash, the relative intensities of the characteristic peaks are different, which can be employed to identify and distinguish different spice samples. Then, using LIBS combined with principal component analysis (PCA) and error back propagation artificial neural network (BP-ANN), the model of classification is established to distinguish different spices. In PCA, the dimension of the spectra of the ash is reduced, and the cumulative contribution rate of the first two PCs exceeds 90%. The samples after dimension reduction by PCA are classified by BP-ANN, and the recognition rate can reach 100%. After 10 cross-verifications, the final recognition accuracy can reach 85.25%. All of the results show the model of classification has the potential in the field of identification and distinction of different spices.

In geological exploration, it is necessary to analyze the geological history according to the rock types. But in places for people to reach, sample collection, and transportation is a costly task. At present, the remote intelligent detection of rock types needs to be further developed. In this study, laserinduced breakdown spectroscopy (LIBS), a sensitive optical technique that can rapidly analyze various elements, is applied to real-time detection and analysis of rock types. Representative rock samples and minerals are selected for spectral analysis and machine learning. The characteristic spectral lines of Ca, Al, Mg, Ti, Si, Na, Fe, K, and Li were observed in the spectra. By comparing the spectra of different samples, the differences among them were discussed.First, principal component analysis (PCA) is used for dimensionality reduction. With the help of PCA, the data are distributed in twodimensional and three-dimensional space and different kinds of rocks and minerals are classified successfully. Then, combined with error back propagation training artificial neural network, the rock and mineral identification model was established, and the recognition rate can reach 100%. The results show that LIBS is a powerful tool for remote intelligent realtime rock detection and classification, and has great application prospects in the exploration of extraterrestrial objects including planets, satellites and asteroids in the future.

The damage of kitchen oil fume to the human body and environment cannot be ignored. Based on laser-induced breakdown spectroscopy (LIBS), five kitchen environments are online in situ detected, including the air scene, fry scene, grill scene, steam scene, and stew scene. In the spectra, characteristic elements such as C, H, O, and N are detected in the fry scene containing oil fume, and metal elements such as Mg, Ca, K, and Na are observed in the grill scene containing charcoal smoke. The spectra of five kitchen environments are tested and compared. In the measurement, except for the air scene, obvious carbon–nitrogen molecular spectral lines are detected. LIBS is combined with principal component analysis and backpropagation artificial neural network system to detect and analyze kitchen fumes. Finally, five kitchen scenes are analyzed and identified based on this system, and the final recognition accuracy is 98.60%.

Pigeon is a kind of poultry with high practicability and economic value, and humidity is an important indicator of the intensive breeding of pigeons, which is closely related to the healthy growth of pigeons. Humidity changes are complex, dynamic, and nonlinear. Accurately predicting humidity values and analyzing their changing trends is crucial to the growth of pigeons. Addressing low prediction accuracy and poor generalization of traditional humidity prediction methods, this paper proposes a combination based on partial least squares (PLS) data dimensionality reduction, Savitzky-Golay (SG) filter, and sparrow optimization algorithm (SSA) Predictive models to improve humidity forecast accuracy. First, the SG filter is used for data smoothing to remove abnormal noise in the data signal to enhance the feature learning ability of the prediction system. Next, the original data is selected through the PLS method to select information higher than the set threshold. Valuable information features are extracted for data dimensionality reduction. In addition, SSA is used to optimize key parameters and structure of the BiGRU model for improved prediction. Finally, a combined prediction model based on SG-PLS-SSA-BiGRU was built to simulate and predict the humidity of the pigeon house. The experimental results demonstrate the proposed model's high accuracy and stability with minimal prediction error fluctuations, fast calculation speed, good feature extraction ability and generalization ability, and is very effective and reliable for predicting pigeon house breeding humidity.