Detection of illicit drug residues from wastewater provides a new route toward community-level assessment of drug abuse that is critical to public health. However, traditional chemistry analytical tools such as high-performance liquid chromatography in tandem with mass spectrometry (HPLC-MS) cannot meet the large-scale testing requirement in terms of cost, promptness, and convenience of use. In this article, we demonstrated ultra-sensitive and portable surface-enhanced Raman scattering sensing (SERS) of fentanyl, a synthetic opioid, from sewage water and achieved quantitative analysis through principal component analysis and partial least-squares regression. The SERS substrates adopted in this application were synthesized by in situ growth of silver nanoparticles on diatomaceous earth films, which show ultra-high sensitivity down to 10 parts per trillion in artificially contaminated tap water in the lab using a commercial portable Raman spectrometer. Based on training data from artificially contaminated tap water, we predicted the fentanyl concentration in the sewage water from a wastewater treatment plant to be 0.8 parts per billion (ppb). As a comparison, the HPLC-MS confirmed the fentanyl concentration was below 1 ppb but failed to provide a specific value of the concentration since the concentration was too low. In addition, we further proved the validity of our SERS sensing technique by comparing SERS results from multiple sewage water treatment plants, and the results are consistent with the public health data from our local health authority. Such SERS sensing technique with ultra-high sensitivity down to sub-ppb level proved its feasibility for point-of-care detection of illicit drugs from sewage water, which is crucial to assess public health.

The interpretation of well-testing data is a key means of decision-making support for oil and gas field development. However, conventional processing methods have many problems, such as the stochastic nature of the data, feature redundancies, the randomness of the initial weights or thresholds, and fluctuations in the generalization ability with slight changes in the network parameters. These result in a poor ability to characterize data features and a low generalization ability of the interpretation models. We propose a new integrated well-testing interpretation model based on a multi-feature extraction method and deep mutual information classifiers (MFE-DMIC). This model can avoid the low model classification accuracy caused by the simple training structures, lacking of redundancy elimination, and the non-optimal classifier configuration parameters. First, we obtained the initial features according to four classical feature extraction methods. Then, we eliminated feature redundancies using a deep belief network and united the maximum information coefficient method to achieve feature purification. Finally, we calculated the interpretation results using a hybrid particle swarm optimization–support vector machine classification system. We used 572 well-testing field samples, including five working stages, for model training and testing. The results show that the MFE-DMIC model had the highest total stage classification accuracy of 98.18% as well as the least of features (nine) compared with the classical feature extraction and classification methods and their combinations. The proposed model can reduce the efforts of oil analysts and allow accurate labeling of samples to be predicted.

Diffuse reflectance spectroscopy (DRS) is significantly affected from the interference of the ambient light and dark current of the instrument. Optical choppers, together with lock-in/synchronous amplification, can overcome these interferences. However, in spectral measurement, the sampling rate of the spectrometer is different from the Δ-pulse sampling, which is not high enough because of the integration time. In addition, the energy distribution is not perfectly concentrated as expected in modulate chopper technology. Therefore, in this study, based on the modulate chopper technique, we proposed a principal frequency component analysis (PFCA) method for DRS. This technique not only effectively eliminated the interference and dark current of the instrument but also improved the measurement precision using the energy of different frequencies. First, experiments were designed to successfully verify the function of optical choppers, eliminating the interference of the ambient light. Second, a set of 64 mixture solutions was designed and measured by DRS using the PFCA method to prove the feasibility of the proposed method. The solution was mixed with intralipid-20% suspension, India ink, and rhodamine B. These samples were analyzed by DRS under different conditions: no-chopper with overlapping and averaging, chopper demodulated by Fourier transform, and chopper demodulated by PFCA. The partial least square regression analysis was implemented to predict the concentration. Compared to the result of three methods, DRS equipped with chopper using the PFCA method showed the best results. The results of this study showed that the PFCA method not only satisfactorily eliminated the interference signals but also extracted useful information as much as possible, improving the analysis accuracy.

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A granary trial was conducted to evaluate the efficacy of protein-enriched pea flour against three common stored-grain insects, Sitophilus oryzae (L.), Tribolium castaneum (Herbst), and Cryptolestes ferrugineus (Stephens). Six 30-t farm granaries were filled with approximately 11 t of barley. The barley was either not treated, treated with protein-enriched pea flour at 0.1% throughout the entire grain mass, or treated at 0.5% throughout the top half of the grain mass. Adult insects were released in screened boxes (two insects per kilogram barley for S. oryzae and T. castaneum 1.4 insects per kilogram barley for C. ferrugineus). Barley was sampled four times during the 70-d trial. The number and mortality of adults and emerged adults in the samples were noted. Four kinds of traps, flight, surface-pitfall, probe-pitfall, and sticky-bar, were placed at different locations in the granaries to estimate the movement of insects. The 0.1% protein-enriched pea flour treatment reduced adult numbers of S. oryzae by 93%, T. castaneum by 66%, and C. ferrugineus by 58%, and reduced the emerged adults by 87, 77, and 77%, respectively. Treating the top half of the barley with 0.5% protein-enriched pea flour had similar effects as treating the entire grain mass with 0.1% pea-protein flour. However, the top-half treatment failed to prevent insects from penetrating into the untreated lower layer. Differences between traps are discussed.