In this article, a tunable pulsed laser induced photoacoustic measurement setup of monitoring glucose concentration was established in the forward mode. In experiments, the time-resolved photoacoustic signal of glucose aqueous solution with different concentrations of 0-300 mg/dl were captured and averaged 512 times, and the photoacoustic peak-to-peak values were recorded using the wavelength scan in NIR region of 1300-2300 nm. The optimal characteristic wavelengths of glucose were determined via the difference spectral and the first derivative spectral algorithm, and correction models between peak-to-peak values of optimal wavelengths and concentration gradients were established using multivariate linear regression algorithm. Experimental results demonstrated that the profile and logarithm shape of time-resolved photoacoustic signal for glucose solutions were in good agreement with photoacoustic theories. The prediction effect of optimal wavelength of 1510 nm was best, its root-mean-square errors of correction and prediction were 12.14 and 8.45 mg/dl, respectively, the correlation coefficient reached 0.9856.

The quality and price of navel oranges vary depending on their geographical origin, thus providing a financial incentive for origin fraud. To prevent this phenomenon, it is necessary to explore a fast, non-destructive, and precise method for tracing the origin of navel oranges. In this study, a total of 490 Newhall navel oranges were selected from five major production regions in China, and the diffuse reflectance near-infrared spectrum in 4000–10,000 cm⁻¹ were non-invasively collected. We examined seven preprocessing techniques for the spectra, including Savitzky–Golay (SG) smoothing, first derivative (FD), multiplicative scattering correction (MSC), combinations of SG with MSC (SG+MSC), SG with FD (SG+FD), MSC with FD (MSC+FD), and three combined (SG+MSC+FD). A one-dimensional convolutional neural network (1DCNN) deep learning model for geographical origin tracing of navel orange was established, and five machine learning algorithms, i.e., partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and back-propagation neural network (BPNN), were compared with 1DCNN. The results show that the 1DCNN model based on the SG+FD preprocessing method achieved the optimal performance for the testing set, with prediction accuracy, precision, recall, and F1-score of 97.92%, 98%, 97.95%, and 97.90%, respectively. Therefore, NIRS combined with deep learning has a significant research and application value in the rapid, nondestructive, and accurate geographical origin traceability of agricultural products.

In this work, photoacoustic spectroscopy was employed to distinguish real blood from fake blood rapidly, accurately, and recoverably. To achieve this goal, a photoacoustic detection system for blood was established in the forward mode. In the experiments, four kinds of animal blood and two kinds of fake blood in a total of 150 groups were used. The time-resolved photoacoustic signal and peak-to-peak values (PPVs) of all blood were captured in 700−1064 nm with intervals of 5 nm. Experimental results show that the amplitudes, profiles, peak-point time, and PPVs are different between real and fake blood. Although the PPVs of real blood are larger than those of the fake ones at 700−850 nm, the differences in PPVs are not obvious at 850−1064 nm, especially when there are spectral overlaps of PPVs. To accurately classify and discriminate real and fake blood, a wavelet neural network (WNN) was used to train 120 groups of blood and test 30 groups of blood. Moreover, the particle swarm optimization (PSO) algorithm was used to optimize the weights and thresholds, as well as the translation and scale factors of the Morlet-liked wavelet basis function of the WNN. Under optimal parameters, the correct rate of the WNN-PSO algorithm was improved from 63.3% to 96.7%. Next, principal component analysis (PCA) was combined into the WNN-PSO algorithm to further improve the correct rate. The results indicate that the correct rate of the PCA-WNN-PSO algorithm with 10 principal components reaches 100 %. Therefore, photoacoustic spectroscopy combined with the PCA-WNN-PSO algorithm exhibits excellent performance in the classification and discrimination of real and fake blood.