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Surface-enhanced Raman scatters (SERS) spectroscopy is a novel detection technology which has advantages of fingerprint, high sensitivity, simple pretreatment and strong anti-interference for water and has been widely used for the analysis of organophosphorus pesticide residues. Furthermore, the intelligent species identification and quantitative analysis of organophosphorus pesticides can be achieved by combing with chemometrics methods. In the actual detection, the classification accuracy of conventional algorithms are limited for the recognition of SERS spectra of some structural analogues. The paper introduces a novel algorithm by the fusion of boosting and support vector machine (SVM) to improve the recognition accuracy of similar SERS spectroscopy of pesticides (ethyl paraoxon and methyl parathion). In the paper, the spectra of the above two pesticides from 600 to1800 cm−1 were firstly measured using dynamic SERS, and the baseline drift of spectra was deducted through adaptive penalty least-square method. The high frequency burr was reduced by the polynomial smoothing. Finally, the classification model was respectively constructed using SVM and AdaBoost-SVM which combined the discrete AdaBoost (the one implementation of boosting) with SVM, and the algorithm performance was quantitatively evaluated using the 5-fold interaction validation method with the classification accuracy. The experimental results show that the overall classification identification of Adaboost-SVM is significantly superior to SVM, and the accuracy increases by nearly 4.23%. Additionally, during the tuning of Co and gstep for the Adaboost-SVM, the effect on the classification performance is relatively small. The phenomenon demonstrates Adaboost-SVM has the excellent robustness.

Fluorescence microscopic imaging in centimeter-deep tissue has been highly sought-after for many years because much interesting in vivo micro-information, such as microcirculation, tumor angiogenesis, and metastasis, may deeply locate in tissue. In this study, for the first time this goal has been achieved in 3-centimeter deep tissue with high signal-to-noise ratio (SNR) and picomole sensitivity under radiation safety thresholds. These results are demonstrated not only in tissue-mimic phantoms but also in actual tissues, such as porcine muscle, ex vivo mouse liver, ex vivo spleen, and in vivo mouse tissue. These results are achieved based on three unique technologies: excellent near infrared ultrasound-switchable fluorescence (USF) contrast agents, a sensitive USF imaging system, and an effective correlation method. Multiplex USF fluorescence imaging is also achieved. It is useful to simultaneously image multiple targets and observe their interactions. This work opens the door for future studies of centimeter-deep tissue fluorescence microscopic imaging.

Two cost-efficient systems were designed and demonstrated for reconstructing ultrasound wave propagation and interference and for understanding the principles of ultrasound imaging and microbubble-enhanced ultrasound imaging. One of the systems was based on a needle hydrophone that was mechanically scanned in an ultrasound focal zone, and the other system was based on a metal wire or needle, with a small diameter, that was translated in an ultrasound focal zone. The advantages and disadvantages of both methods were discussed. These systems are useful for students, educators, and researchers for investigating ultrasound wave properties and understanding ultrasound imaging principles when the budget is limited.