Data transmission between spectroscopy equipment and mobile terminals is critical to realising hand-held field-level monitoring. Currently, on-the-go (OTG) communication technology is a convenient and efficient method of data transmission for mobile devices. However, few people associate spectroscopy equipment with smartphones through the OTG port. This study developed a portable imaging spectrometer with a spectral resolution of approximately 12 nm in the visible-near-infrared band (400–1000 nm). It can be connected to a smartphone through the USB-OTG port to process the spectral signal through the smartphone's system on a chip (SoC). It also displays real-time spectral images of the food samples through the smartphone's screen. Using a support vector machine (SVM) to classify the spectra of the various experimental samples (e.g. eggs and pork), the model prediction accuracy rate is approximately 90%. This further proves the reliability of the proposed smartphone imaging spectrometer for monitoring the freshness of food samples onsite.

Hyperspectral imaging (HSI) combines spectroscopy and 2D imaging to reveal sample composition and properties. Video level HSI helps to observe and analyze molecular features in dynamic processes. However, maintaining a high imaging speed will sacrifice spectral and spatial resolution. For real-time dynamic biological samples monitoring with high spectral and spatial resolution, this work proposes a novel HSI system, which includes a high-speed galvo mirror and a 10Gigabit ethernet port CMOS sensor for spatial scanning and data acquisition. The galvo mirror can scan spatial light, accelerating the collection rate of hyperspectral cubes to the video level. The CMOS sensor can directly collect spectral spatial optical data at a speed of 1600 FPS and synchronously outputs the data. Theoretically, this system can achieve N cubes per second with 624 × (1600/N) x 500 resolutions, whose spectral bands and resolution are 500 and 3-5nm respectively. This is the first-time hyperspectral data has been obtained at such a high throughput and cube rate. Chlorophyll sensing and mouse tumor localization were carried out to verify the system effectiveness. Hyperspectral videos of human palms both under stress and in a relaxed state, and hyperspectral videos of jellyfish navigation through water are recorded, considering the signal-to-noise ratio, the acquisition speed is reduced to one cube per second, and the acquisition cube size is expanded to 624 × 1600 × 500. Then, spectral data is extracted from keyframes of the video to observe changes in molecular information. This promising tool offers great potential for biological living being detected.

The safeguarding of scarce water resources is critically dependent on continuous water quality monitoring. Traditional methods like satellite imagery and automated underwater observation have limitations in cost-efficiency and frequency. Addressing these challenges, a ground-based remote sensing system for the high-frequency, real-time monitoring of water parameters has been developed. This system is encased in a durable stainless-steel shell, suited for outdoor environments, and features a compact hyperspectral instrument with a 4 nm spectral resolution covering a 350–950 nm wavelength range. In addition, it also integrates solar power, Wi-Fi, and microcomputers, enabling the autonomous long-term monitoring of water quality. Positioned on a rotating platform near the shore, this setup allows the spectrometer to quickly capture the reflective spectrum of water within 3 s. To assess its effectiveness, an empirical method correlated the reflective spectrum with the actual chlorophyll a(Chla) concentration. Machine learning algorithms were also used to analyze the spectrum’s relationship with key water quality indicators like total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). Results indicate that the band ratio algorithm accurately determines Chla concentration (R-squared = 0.95; RMSD = 0.06 mg/L). For TP, TN, and COD, support vector machine (SVM) and linear models were highly effective, yielding R-squared values of 0.93, 0.92, and 0.88, respectively. This innovative hyperspectral water quality monitoring system is both practical and reliable, offering a new solution for effective water quality assessment.