Near infrared (NIR) spectroscopy allows rapid estimation of quality traits in fresh fruit. Several portable spectrometers are available in the market as a low-cost solution to perform NIR spectroscopy. However, portable spectrometers, being lower in cost than a benchtop counterpart, do not cover the complete near infrared (NIR) spectral range. Often portable sensors either use silicon-based visible and NIR detector to cover 400–1000 nm, or InGaAs-based short wave infrared (SWIR) detector covering the 900–1700 nm. However, these two spectral regions carry complementary information, since the 400–1000 nm interval captures the color and 3rd overtones of most functional group vibrations, while the 1st and the 2nd overtones of the same transitions fall in the 1000–1700 nm range. To exploit such complementarity, sequential data fusion strategies were used to fuse the data from two portable spectrometers, i.e., Felix F750 (~400–1000 nm) and the DLP NIR Scan Nano (~900–1700 nm). In particular, two different sequential fusion approaches were used, namely sequential orthogonalized partial-least squares (SO-PLS) regression and sequential orthogonalized covariate selection (SO-CovSel). SO-PLS improved the prediction of moisture content (MC) and soluble solids content (SSC) in pear fruit, leading to an accuracy which was not obtainable with models built on any of the two spectral data set individually. Instead, SO-CovSel was used to select the key wavelengths from both the spectral ranges mostly correlated to quality parameters of pear fruit. Sequential fusion of the data from the two portable spectrometers led to an improved model prediction (higher R² and lower RMSEP) of MC and SSC in pear fruit: compared to the models built with the DLP NIR Scan Nano (the worst individual block) where SO-PLS showed an increase in R²_p up to 56% and a corresponding 47% decrease in RMSEP. Differences were less pronounced to the use of Felix data alone, but still the R²_p was increased by 2.5% and the RMSEP was reduced by 6.5%. Sequential data fusion is not limited to NIR data but it can be considered as a general tool for integrating information from multiple sensors.

近红外（NIR）光谱可以快速估计新鲜水果的质量特征。市场上有几种便携式光谱仪可作为执行近红外光谱的低成本解决方案。但是，便携式光谱仪的成本低于台式光谱仪，无法覆盖完整的近红外（NIR）光谱范围。通常，便携式传感器要么使用基于硅的可见光和NIR检测器来覆盖400–1000 nm，要么使用基于InGaAs的短波红外（SWIR）检测器来覆盖900–1700 nm。但是，这两个光谱区域带有互补信息，因为400-1000 nm的间隔捕获了大多数官能团振动的颜色和第三泛音，而相同过渡的第一和第二泛音落在1000-1700 nm范围内。为了利用这种互补性，顺序数据融合策略用于融合来自两个便携式光谱仪的数据，即Felix F750（〜400–1000 nm）和DLP NIR Scan Nano（〜900–1700 nm）。特别是，使用了两种不同的顺序融合方法，即顺序正交偏最小二乘（SO-PLS）回归和顺序正交协变量选择（SO-CovSel）。SO-PLS改善了梨果实中水分含量（MC）和可溶性固形物含量（SSC）的预测，从而导致精度无法单独建立在两个光谱数据集上的任何一个模型上。取而代之的是，使用SO-CovSel从两个主要与梨果实质量参数相关的光谱范围中选择关键波长。来自两个便携式光谱仪的数据的顺序融合导致改进的模型预测（较高的R梨果实中MC和SSC的²和更低的RMSEP）：与使用DLP NIR Scan Nano（最差的单个区块）构建的模型相比，其中SO-PLS的R ² _p增加高达56％，相应的为47％ RMSEP降低。单独使用Felix数据的差异不太明显，但R ² _p仍增加了2.5％，RMSEP减少了6.5％。顺序数据融合不限于NIR数据，还可以视为整合来自多个传感器的信息的通用工具。