The quality of milk is related to its geographical origin. In addition, the geographical origin of milk plays an important role in its commercial value. Therefore, this research aimed to design an identification system including a portable near‐infrared (NIR) spectrometer and feature extraction algorithms to authenticate the geographical origin of milk. In order to improve the classification accuracy, fuzzy uncorrelated discriminant transformation (FUDT) was used to deal with the NIR spectra of milk collected by a portable NIR spectrometer. In this system, Savitzky–Golay (SG) algorithm and principal component analysis (PCA) were used to remove noise and reduce dimensionality, respectively. Then, three feature extraction algorithms, linear discriminant analysis (LDA), uncorrelated discriminant transformation (UDT), and FUDT were applied to separate the NIR spectra. Finally, K‐nearest neighbor (KNN) classifier was utilized to assess the performance of this identification system. The results showed that the maximum classification accuracies of FUDT, UDT, and LDA were 98.67%, 97.33%, and 93.33%, respectively. The results confirmed the great potential for authenticating the geographical origin of milk by the combination of the portable NIR spectrometer and FUDT. Practical Applications The geographical origin of milk is vitally important to evaluate the milk quality in dairy market. Compared with traditional detection methods, NIR spectroscopy is considered to be a fast, accurate, and nondestructive detection method, so it is widely used in the field of food detection. In this article, FUDT with portable NIR spectrometer can be used to detect the adulteration of geographical origin in milk correctly and rapidly. The experimental result indicated that application potential in identifying the geographical origin of milk.

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China accounts for more than 22% of the total energy consumption worldwide. Building energy consumption, among which consumption in public buildings was about 40% took the second place. With the problems of high energy waste, error rate and complexity of the control systems available, an indoor intelligent lighting system based on occupants’ location is proposed in this paper to improve the energy efficiency of the current lighting systems indoors. The transmission model of electromagnetic wave in free space is optimized in both aspects of reference signal strength and attenuation coefficient radiation in complex environment dynamically based on which occupants’ positions are obtained. The smart lighting system will turn on or off corresponding lights adaptively to provide a more energy efficient platform. Experimental results show that the proposed system is able to improve the energy efficiency of indoor lighting by at least 15%, with a lower error rate below 2% compared with the existing lighting systems based on voice control.

Excessive pesticide residues on Chinese cabbage will be harmful to people's health. Therefore, an identification system was designed for qualitative analysis of lambda-cyhalothrin residues on Chinese cabbage leaves. In order to extract discriminant information from mid-infrared (MIR) spectra of Chinese cabbage effectively, fuzzy uncorrelated discriminant vector (FUDV) analysis was proposed by introducing the fuzzy set theory into uncorrelated discriminant vector (UDV) analysis. In this system, the Cary 630 FTIR spectrometer was used to scan four samples of Chinese cabbage with different concentrations of lambda-cyhalothrin. The MIR spectra were preprocessed by standard normal variable (SNV) and Savitzky-Golay smoothing (SG). Next, the high-dimensional MIR spectra were processed for dimension reduction by principal component analysis (PCA). Furthermore, UDV, FUDV, and some other discriminant analysis algorithms were used for feature extraction, respectively. Finally, the K-nearest neighbor (KNN) classifier was employed to classify the data. The experimental results showed that when FUDV was used as the feature extraction algorithm, the identification system reached the maximum classification accuracy of 100%. The results indicated that FUDV combined with MIR spectroscopy was an effective method to identify lambda-cyhalothrin residues on Chinese cabbage.

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