A Portable Laser Spectroscopic System for Measuring Nitrous Oxide Emissions on Fertilized Cropland

Nitrous oxide (laughing gas, N2O) is a relevant greenhouse gas. Agriculture contributes significantly to its emissions. As nitrogen fertilization has been identified as one of the main sources of N2O, controlled application and reduction of the amount of fertilizer adapted to crop demand is essential to reduce N2O emissions. This requires detailed studies of the local distribution of the N2O emission fluxes on different croplands. Consequently, frequent spatially resolved field measurements of N2O concentrations are needed. A precision in the ppb range close to the ambient N2O level of 333 ppb is necessary. Tunable laser absorption spectroscopy using quantum-cascade lasers (QCL) as a light source is an established technique for the measurement of N2O traces. We present the development and validation of a compact portable setup for on-site measurement of N2O emissions from the soil. The setup differs from previous solutions by using an interband cascade laser (ICL), which has significantly lower power consumption compared to a QCL. The portable measurement setup allows N2O emission fluxes to be determined with a precision of 3.5% with a measuring duration of 10 min. The developed system enables the detection of increased N2O emissions because of the fertilization of fields. High N2O emission fluxes are indicators of the overfertilization of the field. Directly after fertilization, N2O fluxes between 2.9 and 5.3 µL m-2 min-1 depending on the gas acquisition site are measured during the field tests. Over time, the fluxes decrease. The obtained results compare well with data from more precise but also more complex and maintenance-intensive instruments for atmospheric research. With this system, the soil moisture as well as the air humidity and air temperature are recorded. Strong influences on N2O fluxes by soil moisture were observed. The presented measurement system is a contribution to the establishment of mobile N2O screening systems that are robust in the field and suitable for comprehensive and routine detection of N2O emissions from soil.

Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality

Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.

Towards Interband Cascade lasers on InP Substrate

In this study, we propose designs of an interband cascade laser (ICL) active region able to emit in the application-relevant mid infrared (MIR) spectral range and to be grown on an InP substrate. This is a long-sought solution as it promises a combination of ICL advantages with mature and cost-effective epitaxial technology of fabricating materials and devices with high structural and optical quality, when compared to standard approaches of growing ICLs on GaSb or InAs substrates. Therefore, we theoretically investigate a family of type II, "W"-shaped quantum wells made of InGaAs/InAs/GaAsSb with different barriers, for a range of compositions assuring the strain levels acceptable from the growth point of view. The calculated band structure within the 8-band k·p approximation showed that the inclusion of a thin InAs layer into such a type II system brings a useful additional tuning knob to tailor the electronic confined states, optical transitions' energy and their intensity. Eventually, it allows achieving the emission wavelengths from below 3 to at least 4.6 μm, while still keeping reasonably high gain when compared to the state-of-the-art ICLs. We demonstrate a good tunability of both the emission wavelength and the optical transitions' oscillator strength, which are competitive with other approaches in the MIR. This is an original solution which has not been demonstrated so far experimentally. Such InP-based interband cascade lasers are of crucial application importance, particularly for the optical gas sensing.

Genetic analysis on infrared-predicted milk minerals for Danish dairy cattle

A group of milk components that has shown potential to be predicted with milk spectra is milk minerals. Milk minerals are important for human health and cow health. Having an inexpensive and fast way to measure milk mineral concentrations would open doors for research, herd management, and selective breeding. The first aim of this study was to predict milk minerals with infrared milk spectra. Additionally, milk minerals were predicted with infrared-predicted fat, protein, and lactose content. The second aim was to perform a genetic analysis on infrared-predicted milk minerals, to identify QTL, and estimate variance components. For training and validating a multibreed prediction model for individual milk minerals, 264 Danish Jersey cows and 254 Danish Holstein cows were used. Partial least square regression prediction models were built for Ca, Cu, Fe, K, Mg, Mn, Na, P, Se, and Zn based on 80% of the cows, selected randomly. Prediction models were externally validated with 8 herds based on the remaining 20% of the cows. The prediction models were applied on a population of approximately 1,400 Danish Holstein cows with 5,600 infrared spectral records and 1,700 Danish Jersey cows with 7,200 infrared spectral records. Cows from this population had 50k imputed genotypes. Prediction accuracy was good for P and Ca, with external R² ≥ 0.80 and a relative prediction error of 5.4% for P and 6.3% for Ca. Prediction was moderately good for Na with an external R² of 0.63, and a relative error of 18.8%. Prediction accuracies of milk minerals based on infrared-predicted fat, protein, and lactose content were considerably lower than those based on the infrared milk spectra. This shows that the milk infrared spectrum contains valuable information on milk minerals, which is currently not used. Heritability for infrared-predicted Ca, Na, and P varied from low (0.13) to moderate (0.36). Several QTL for infrared-predicted milk minerals were observed that have been associated with gold standard milk minerals previously. In conclusion, this study has shown infrared milk spectra were good at predicting Ca, Na, and P in milk. Infrared-predicted Ca, Na, and P had low to moderate heritability estimates.

Design of a Curved Shape Photonic Crystal Taper for Highly Efficient Mode Coupling

The design and modeling of a curved shape photonic crystal taper consisting of Si rods integrated with a photonic crystal waveguide are presented. The waveguide is composed of a hexagonal lattice of Si rods and optimized for CO₂ sensing based on absorption spectroscopy. We investigated two different approaches to design a taper for a photonic crystal waveguide in a hexagonal lattice of silicon rods. For the first approach (type 1), the taper consists of a square lattice taper followed by a lattice composed of a smooth transition from a square to a hexagonal lattice. In the second approach (type 2), the taper consists of a distorted hexagonal lattice. Different shapes, such as convex, concave, and linear, for the curvature of the taper were considered and investigated. The structure of the taper was improved to enhance the coupling efficiency up to 96% at a short taper length of 25 lattice periods. The finite-difference time-domain (FDTD) technique was used to study the transmission spectrum and the group index. The study proves the improvement of coupling using a curved shape taper. Controlling the group index along the taper could be further improved to enhance the coupling efficiency in a wider spectral range.

A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra

Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R²v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set.

Tunable Metamaterial with Gold and Graphene Split-Ring Resonators and Plasmonically Induced Transparency

In this paper, we propose a metamaterial structure for realizing the electromagnetically induced transparency effect in the MIR region, which consists of a gold split-ring and a graphene split-ring. The simulated results indicate that a single tunable transparency window can be realized in the structure due to the hybridization between the two rings. The transparency window can be tuned individually by the coupling distance and/or the Fermi level of the graphene split-ring via electrostatic gating. These results could find significant applications in nanoscale light control and functional devices operating such as sensors and modulators.

Infrared Spectroscopy as a Versatile Analytical Tool for the Quantitative Determination of Antioxidants in Agricultural Products, Foods and Plants

Spectroscopic methods provide with very useful qualitative and quantitative information about the biochemistry and chemistry of antioxidants. Near infrared (NIR) and mid infrared (MIR) spectroscopy are considered as powerful, fast, accurate and non-destructive analytical tools that can be considered as a replacement of traditional chemical analysis. In recent years, several reports can be found in the literature demonstrating the usefulness of these methods in the analysis of antioxidants in different organic matrices. This article reviews recent applications of infrared (NIR and MIR) spectroscopy in the analysis of antioxidant compounds in a wide range of samples such as agricultural products, foods and plants.

Electrically pumped mid-infrared random lasers

Electrically pumped random lasers, operating in the mid-infrared regime at λ ≈ 10 μm, are realized for the first time. Randomly distributed air holes are patterned onto a quantum cascade wafer emitting in the transverse-magnetic (TM) polarization. The advantage of employing TM polarized gain medium is that TM modes are more effectively confined in the gain region, and thus improve lasing efficiency and vertical confinement compared to TE modes.

Real-time N2O gas detection system for agricultural production using a 4.6-µm-band laser source based on a periodically poled LiNbO3 ridge waveguide

This article describes a gas monitoring system for detecting nitrous oxide (N2O) gas using a compact mid-infrared laser source based on difference-frequency generation in a quasi-phase-matched LiNbO3 waveguide. We obtained a stable output power of 0.62 mW from a 4.6-μm-band continuous-wave laser source operating at room temperature. This laser source enabled us to detect atmospheric N2O gas at a concentration as low as 35 parts per billion. Using this laser source, we constructed a new real-time in-situ monitoring system for detecting N2O gas emitted from potted plants. A few weeks of monitoring with the developed detection system revealed a strong relationship between nitrogen fertilization and N2O emission. This system is promising for the in-situ long-term monitoring of N2O in agricultural production, and it is also applicable to the detection of other greenhouse gases.

Mid-Infrared Tunable Resonant Cavity Enhanced Detectors

Mid-infrared detectors that are sensitive only in a tunable narrow spectral band are presented. They are based on the Resonant Cavity Enhanced Detector (RCED) principle and employing a thin active region using IV-VI narrow gap semiconductor layers. A Fabry-Pérot cavity is formed by two mirrors. The active layer is grown onto one mirror, while the second mirror can be displaced. This changes the cavity length thus shifting the resonances where the detector is sensitive. Using electrostatically actuated MEMS micromirrors, a very compact tunable detector system has been fabricated. Mirror movements of more than 3 μm at 30V are obtained. With these mirrors, detectors with a wavelength tuning range of about 0.7 μm have been realized. Single detectors can be used in mid-infrared micro spectrometers, while a detector arrangement in an array makes it possible to realize Adaptive Focal Plane Arrays (AFPA).